Edge Based Interpolation with Refinement Algorithm Using Edge Strength Filter for Digital Camera Images

Turkish Journal of Physiotherapy and Rehabilitation; 32(2) ISSN 2651-4451 | e-ISSN 2651-446X

EDGE BASED INTERPOLATION WITH REFINEMENT ALGORITHM USING EDGE STRENGTH FILTER FOR DIGITAL CAMERA IMAGES

S. MARKKANDAN 1, LAKSHMI NARAYANAN 2, ROBERT THEIVADAS J 3, P. SURESH 4* 1 Department of ECE, SRM TRP Engineering College, Trichy, Tamilnadu. India 2 ECE Department, Gojan School of Business and Technology, Chennai, Tamilnadu. India 3 Director, Digialtyic Technologies, Chennai, Tamilnadu. India 4 Dept of ECE, Veltech Rangarajan Dr Sagunthala R and D Institute of Science and Technology, Chennai, Tamilnadu, India. *[email protected]

ABSTRACT

A digital camera has become popular as many people are preferring a digital camera to take a picture. After recording the image, the performance in the digital camera should enable the user to view the captured image. The important role in the image processing chain is the interpolation of color-filter-array (CFA) or demosaicing. The color filter array (CFA) is the commercial framework widely adopted in the modern digital camera. The interpolation process has to be performed when the color information of the original image is filtered out. The reduction in the size and the cost of the camera depend on the sensor used in the digital camera with the color-filter-array (CFA). At each pixel position, only one color value can be estimated using CFA. The full-color image can be obtained through the estimation of all three colors at each pixel position and this process is known as demosaicing. The computer images are recognized with the usage of red (R), green (G), blue (B). The color image requires accurate edge related information which can be attained through edge oriented filter. With the available color, it is necessary to determine the remaining two colors to obtain the entire full-colored image. The missing component undergoes the interpolation process. The edge direction in the Bayer color-filter-array (CFA) is determined using the edge adaptive color demosaicing. The edge direction depends on spatial correlation on the Bayer color difference plane. The most common array is the Bayer CFA and this array involved in the measurement of green (G) image on a quincunx grid and, red (R) and blue (B) images on the rectangular grid. The human visual lies in the medium wavelength where the G images are measured at the higher sampling rate. The artifacts available in the reconstructed image undergoes the refinement process where it separates the low and high frequency using the low pass filter. The main aim of the proposed system model is to produce a high-quality image at a low consumption cost.

Keywords: Color artifacts, Color filter array, Demosaicing, Edge strength filter, refinement process, Bayer pattern.

I. INTRODUCTION Electronic devices such as mobile phones, digital cameras, and wireless personal digital assistants adopted the color filter array (CFA) implanted above the single image sensor to enable visualization of the captured images [1]. The sensors used in the digital camera usually of complementary metal-oxide-semiconductor type (CMOS) or a charge- coupled-device (CCD). The sensor is a monochromatic device where each sensor cell is provided with a specific filter and the CFA data is recognized for producing a gray-scale image. The original color image is produced by undergoing a demosaicing process where the interpolation of the spectrum is used to determine the mislaid color at each location of spatial in the CFA images ([2] and [3]).

The demosaicing process produces different types of artifacts such as aliasing and color shift because of under- sampling. To overcome the consequences due to the artifacts, the manufacturers design the optical path with a burning filter [4]. The advantages of using a filter in digital cameras are a reduction in sharpness and image resolution. Insufficient focusing due to the movement of camera and sensors presentation produces blur images. The image blurring is overcome by introducing the digital camera with an enhanced visual quality where the www.turkjphysiotherrehabil.org 981

Turkish Journal of Physiotherapy and Rehabilitation; 32(2) ISSN 2651-4451 | e-ISSN 2651-446X sharpening technique is used to sharpen the output of the demosaicing process ([4] and [5]). The fine details and edges of the images are visible at the high-frequency in the image sharpening stage ([6] and [7]). The information of edge is essentially important for image development and should be available to the human perception in an imaging system ([8] and [9]).

The color image is obtained by different methodologies at a different time. In the early stage, the color images are obtained through the telegraph printer. Bartlane system is also adopted in the processing of color images to generate a high-quality image. The digital images are used in real time applications such as medical imaging, remote earth resources, and astronomy, geography, nuclear machine, enforcement law, industry, archeology, etc. A digital camera is more effective in generating a color image. The color image is formed by combining the pixel information where the pixel unit consists of programmable color in the images. The image processing includes the three basic colors which are familiarly known as RGB (red, blue, and green) and it is considered as an ideal in creating the images. The application includes the processing of images to make use of this RGB color as it is highly correlated and hence, it is effective in color displays. The intensity and chrominance of light are the features considered in the color image creation where each pixel can only hold three color values [10].

Color-filter-array (CFA) is more important in a single-sensor imaging pipeline. Beneath the CFA, a monochromatic sensor is utilized to produce a low-resolution colored image. In a single-sensor image pipelining, the basic color used is red, blue, and green. The color filter in the CFA is decided by the manufacturer in designing image-enabled consumer electronic devices. This might affect both the computational efficiency and the performance of the demosaicing output. Thus, the CFA has a great impact on the visual quality of the color images [1].

II. RELATED WORK Demosaicing is an important feature in the digital camera to process the images. The improper working of the demosaicing algorithm results in poor image quality and hence, it is more challenging to provide a good quality image. The demosaicing algorithm was developed for the Bayer pattern as it was more effective in obtaining a good quality image. In this, the interpolation technique has been adapted to obtain a quality image. The main advantage of the interpolation technique is that it can correlate among different color channels [11].

The color filter array (CFA) interpolation technique included a normalized color-ratio for effective image quality. The input entering the interpolation stage makes use of the linear shifts to reduce the edge variation effects. Then followed by this process linear scaling and shifting operations were performed to reduce the color-ratio variations in the interpolator’s input [12]. Then an effective high performance influenced iterative algorithm was proposed for a CFA demosaicing [13]. This proposed algorithm occupied the major work in the iteration process in the color difference domain. The spatial criterion was adapted to control the misregistration and zipper artifacts in the demosaiced images. The missing color values were determined through the algorithm of Lukac pattern from the objective and subjective comparison [14].

A novel method was adapted to demosaic the images depending on a posterior decision and directional filtering. The quality of the reconstructed images was improved through further refining steps [15]. A new method of CFA demosaicing was provided with two successive stages. The first step included the correlation of spatial and spectral values with the neighboring pixels to determine the missing color components. The post-processing step was recognized in suppressing the demosaicing artifacts by combining the spectral correlation with the median filter of inter-channel differences [16]. The artifact can be minimized by using an interpolation CFA method in which green images are sampled with the red and blue color images [17].

The Bayer pattern is the subject of more recent approaches. The Bayer and RGB patterns were the key components of the proposed method. In the color pictures, the utility of each pattern was calculated. The multiscale color gradients are the foundation of the interpolation CFA system. The relationship between the different gradients was given by the Bayer and Lukac patterns. The absolute image was obtained through the association of vertical and horizontal color difference gradients [11].

Computer vision was stimulated to automatically recognize the object and provide the information related to the object. The computer vision main was to enable the visualization of humans which was developed from the conventional technique included the attribute such as laboratory analysis and the utilization of artificial intelligence technique (AI) [18]. The analysis of images was performed in combination with the lighting system. The steps www.turkjphysiotherrehabil.org 982

Turkish Journal of Physiotherapy and Rehabilitation; 32(2) ISSN 2651-4451 | e-ISSN 2651-446X involved in the image analysis were image capturing, image preprocessing, image segmentation, image measurement, and image interpretation [19].

III. SYSTEM MODEL: A camera that captures images in digital memory is known as a digital camera. Digital cameras utilize CCD/CMOS (Charge Coupled Device/ Complementary Metal Oxide Semiconductor) to capture and process the images. It consists of sensors. Usually, the camera contains three sensors in capturing the primary colors red, green, and blue colors separately. The detriment of this camera is that the cost and size of the camera are high due to the three sensors. Size reduction and low-cost are achieved through the present-day digital cameras use single-chip CCD or CMOS sensors, where the single sensor is covered with Color Filter Array (CFA). The single sensor framework in a digital camera is shown in Figure 1. The CFA is an array pattern that consists of a filter set in an interleaved pattern and each array consist of only one color. As a result of this arrangement, each sensor sample receives a single color from three primary color components: red, green, and blue, at each pixel location. Each pixel location has a single color portion, and the missing two colors must be obtained from neighboring pixels. Demosaicing or CFA interpolation is the term for this technique.

Figure 1: Single sensor framework in digital camera.

Digital cameras use different types of CFA patterns which composes of primary colors and secondary colors. Some of them are Bayer pattern, lukac pattern, vertical stripes, yamanka pattern, pseudo-random pattern, burtoni CFA pattern [29]. Among the entire pattern, the Bayer CFA pattern is primarily used in the demosaicing process. Bayer pattern consists of a major percentage with a green component, and the remaining percentage is occupied by red and the blue component is represented in Figure 2.

The cost is the major factor that affects the competitiveness of a digital still camera (DSC). The design of low-cost DSC produces a color image with increased quality performance utilizes the three basic colors namely, red, blue, and green. The cost can be reduced by employing single charged-coupled devices (CCDs) at each pixel position and depending on the available devices in the surrounding it undergoes the interpolation with the filtered color channel. The main aim is to make the structural arrangement of the color filter visible to human beings.

Figure 2: Color-filter-array (CFA) Bayer pattern.

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Turkish Journal of Physiotherapy and Rehabilitation; 32(2) ISSN 2651-4451 | e-ISSN 2651-446X

In the Bayer CFA, all pixels are arranged to depend on the fixed ratio (R: G: B) = (0.299: 0.587: 0.114). The major portion in the Bayer pattern is occupied by green color as it is highly sensitive to human eyes. With the known value it is possible to obtain the mislaid colors of the same channel. Bilinear and edge-directed interpolations are some of the interpolation technique [34].

The better image quality can be obtained through the interpolation of inter-channel with the provided basic colors and it is challenging to develop it as a model. The one-dimensional color difference in various channels can be obtained by using a median filter [35]. Later the development is made on the linear interpolation among different colors and it is simple to perform [36]. Depending on the edge sense, the weight coefficient is analysed using the interpolation methodology [37].

Alternative green-red rows and blue-green rows are arranged in the Bayer pattern. For the determination of missing pixels, the adjacent known pixels are considered. Most of the demosaicing algorithm uses a Bayer CFA pattern because of its elementary design. The demosaicing process is carried out by using different interpolation techniques. The earliest proposed interpolation method for images is the nearest neighbor interpolation, bilinear interpolation [21], cubic spline interpolation [20] which are used in the demosaicing process. These techniques estimate the missing pixels but the quality of an image is less. These interpolation techniques produce the image with blurring effect, false color, and zipper effects. To overcome the drawbacks of the earliest techniques, many new interpolation techniques are introduced in the last decades. Some of the interpolation techniques are adaptive weighted edge interpolation, adaptive color plane interpolation, edge sensing interpolation, green edge interpolation.

An iterative algorithm like highly effective iterative demosaicing [25], the demosaicing algorithm is classified into the initial stage and iterative stage. Constant color difference rule [23], [24], which is the common approach, used in their iterative stage, but it falls short to reduce the zipper effect along edges. In [21], modified edge sensing, interpolation is used to determine the mislaid green channel and its discrete wavelet transform to separate high and low-frequency components. The edge directed interpolation technique is used [28] to determine the missing color components. This modus produces sharper edges and fewer color artifacts in the image. Keigo Hirakawa [27] uses a filter bank technique for directional interpolation and non-linear iterative procedures to reduce aliasing, misguidance artifacts. In [28], the author used an edge adaptive algorithm to determine a pattern of line edges, it reduces the artifact along edges. In ECI [31], the color difference between the luminance and chrominance component is determined. On the other hand, it introduces false color in the image. Likewise, several methods are proposed but most of the algorithms fail to utilize the edge information. In this proposed work, the edge information is effectively utilized for the interpolation algorithm. In this proposed work adaptive color plane interpolation, bilinear interpolation, and constant color difference rules were used.

IV. PROPOSED ALGORITHM The proposed algorithm architecture is shown in fig.3. Initially, the input image is modified into a Bayer CFA pattern, then the edge strength of the image is determined using the edge strength filter, which is used to decide for further interpolation technique. The green channel interpolation is done using the vertical and horizontal cost. Based on the estimated green channel, the missing red and blue channels are estimated. Further to increase the performance, the refinement process is implemented.

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Turkish Journal of Physiotherapy and Rehabilitation; 32(2) ISSN 2651-4451 | e-ISSN 2651-446X

Figure 3: Block diagram of proposed technique

Edge strength filter: Edge information is an important parameter in image processing. Many edge detection algorithm likes canny filter and Sobel filter are used, but it detects only the presence of edge structure and it does not provide information such as sharpness, brightness, and luminance about the pixels. Most of the demosaicing algorithm uses a constant color difference assumption, which fails across edges.

If edge information is utilized effectively, then the non-correlated color difference can be easily avoided and this increases the demosaicing performance. For this purpose, the proposed algorithm introduces a new filter called edge oriented filter which enables a transition of information in a free luminance.

For given 5x5 image size, the edge strength for pixel location G13 could be formulated

G−GG−G ES=719+917+B−B+R−R(1) G132 28181214

where ESG represents the edge strength at pixel location PG 13 13 .

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Turkish Journal of Physiotherapy and Rehabilitation; 32(2) ISSN 2651-4451 | e-ISSN 2651-446X

An edge strength map of the input can also be generated by applying the edge strength to all available pixels in the image. The mosaicked picture does not have total luminance details for each pixel since each pixel only has one hue. As a result, the edge intensity filter can be applied to the mosaicked image alone.

The edge intensity of each pixel can be measured by looking at the pixels around it. For example, the inclination difference for the green center pixel portion is obtained from a green channel, while the remainder is obtained from the red and blue channels. As a consequence, the edge strength map is generated by measuring edge strength at each pixel, which is then used to quantify horizontal and vertical expense.

Green channel interpolation: The green channel in the Bayer pattern contains more information because it is measured at a high sampling rate than the red and blue channels. Since the green channel consists of more information, it is necessary to determine the green channel accurately. Various interpolation techniques are introduced to determine the missing green component. Edge directed interpolation [32] detects the local spatial information in the neighboring pixels. Chung- Yen Su [25] introduced weighted edge interpolation to determine the green channel which is based on the gradient and weight. The effective color interpolation [27] method determines the image spectral correlation using bilinear interpolation and the mislaid green elements are determined by utilizing the average difference value of neighboring colors. In the proposed system, for green channel interpolation, a hard decision is made by using an edge strength filter. For this purpose, the horizontal and vertical difference costs are determined from the edge strength filter. The green channel interpolation is done either horizontally or vertically, depends upon the difference cost, which is formulated as,

n  m−1  HC =  (ES − ES ) i, j  i, j i, j+1  i=1  j=1  n−1  m  VC =  (ES − ES ) (2) i, j  i, j i+1, j  i=1  j=1 

ES HC VC Here, i, j are edge strength filter outputs and i, j , i, j are the horizontal and vertical difference cost respectively at pixel location (i, j). This cost estimation is also used in the interpolation of a red and blue plane and refinement process.

The horizontal difference cost estimation is done horizontally (considering pixels in the same row) and vertical difference cost estimation is done vertically (considering pixels in the same column). The target pixel depends on the low-cost difference. In comparison with vertical difference cost, the horizontal cost is low and hence, the target pixel is read as horizontal and vice versa. After all the pixels are marked, green channel interpolation is done based on the final direction label.

The green channel interpolation in red location (in pixel location (3,3) is given as,

~H ~H ~H ~ G3,3−R3,3G3,2−R3,2G3,4−R3,4 G3,3=R+ + + , 3,3 2 4 4 ifHCVC i,j i,j

~V ~V ~V ~ G3,3−R3,3G2,3−R2,3G4,3−R4,3 G3,3=R+ + + , 3,3 2 4 4 ifVCHC(3) i,j i,j

~H ~H ~V ~V Where, G,R,G,Rare the directional estimation. The green channel interpolation in the blue location can be determined similarly by replacing R by B. The directional estimations can be determined using adaptive color www.turkjphysiotherrehabil.org 986

Turkish Journal of Physiotherapy and Rehabilitation; 32(2) ISSN 2651-4451 | e-ISSN 2651-446X interpolation. This algorithm is used to determine blue and red mislaid components in green pixels and vice versa. The directional estimation is calculated as follow,

~H G3,3+G3,52*R3,4−R3,2−R3,6 G3,4= + 2 4

~V G2,4+G4,42*R3,4−R1,4−R5,4 G3,4= + 2 4

~H R3,2+R3,42*G3,3−G3,1−G3,5 R3,3= + 2 4 (4)

Substituting B in the place of R in the above equations helps in determining the green channel interpolation at a blue pixel location. The mislaid red and blue channel is obtained from the green channel estimation.

Interpolation of the Red and Blue Channels: Following the restoration of the green portion, the incomplete blue and red elements must be interpolated. Bayer CFA, green channel interpolation, and the cost discrepancy between horizontal and vertical cost are used to recreate red and blue elements. Color difference B-G and R-G are used for the estimation of red and blue components instead of using R and B directly [33]. Generally, in most papers, the red and blue interpolation algorithm uses constant color difference rule and interpolation of bilinear, the reconstruction of red in a blue component is performed with some modification and vice versa. Utilizing the bilinear interpolation technique, the green pixel is reconstructed with blue and red components. This technique is used because in most of the approaches the interpolation of red and blue pixels in a green component is high and hence, this technique leads to a low computational cost, low complexity and provides better performance.

The estimation of blue and red from green pixels includes an interpolation of bilinear over a color difference estimation technique. The missing red in a green pixel are formulated as,

~ ~ ~ (R3,2 − G 3,2 ) + (R3,4 − G 3,4 ) R3,3 = G3,3 + 2

~ ~ ~ (R3,4−G3,4)+(R5,4−G5,4) R4,4=G4,4+ 2 (5)

~ Here, G is the estimated value of green pixel and the blue channel interpolation at green pixel is acquired by substituting B in place of R in above equations.

Based on the estimated red and blue component, vertical and horizontal difference cost estimation, the blue channel interpolation in the red component, and red channel interpolation in a blue component are done. The missing blue component in a red location are estimated as follow,

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Turkish Journal of Physiotherapy and Rehabilitation; 32(2) ISSN 2651-4451 | e-ISSN 2651-446X

If HC3,4  VC3,4 Then, ~ ~ ~ ~ ~ (B 2,4 − R 2,4 ) + (B 4,4 − R 4,4 ) B 3,4 = R + 3,4 2 Else, ~ ~ ~ ~ ~ (B 3,3 − R 3,3 ) + (B 3,5 − R 3,5 ) B 3,4 = R3,4 + (6) 2

Where HC and VC are the horizontal and vertical difference cost estimated from the edge strength map and are the estimated red and blue component in green pixels. In blue pixels, the value of red interpolation is obtained through the same strategy. By the end of these steps, separate full green, blue and red planes are reconstructed. The color image can be developed by combining these planes. Still, the minimum range of artifacts is available in the image and the artifacts can be reduced by further involving in a refinement process.

Refinement process: The missing components are determined followed by the refinement process in demosaicing. The main purpose of the refinement process is to increase performance efficiency with the simultaneous reduction in artifacts. The color values and the difference cost are the features included in the refinement process. The refinement process includes the filter design approximation for the development of a system model. The high-frequency component usually contains more artifacts. The high band inter-channel correlation is performed in a three-color plane that can separate both the high and low-frequency components. The low-frequency components remain unchanged, as it is less correlated whereas the high-frequency components are replaced with a Bayer component. The refinement process is illustrated with a green value and the decomposition of green value is expressed as follows,

(7)

GL is depicted as a low frequency variable in this diagram.

GH is defined as a variable with a high frequency.

The below is the relationship between the red and blue values:

(8)

The high-frequency components are obtained from the difference value of low frequency where the low frequency is selected by the low pass filter (LPF). The refinement process involved at each pixel is supported by the FIR low pass filter. The FIR filter frequency value is given as follows,

(9)

The refinement process decides the cost of the image. The filter used in the refinement process enables us to separate the high and low-frequency components. The red and blue frequency is replacing the high-frequency value of the green channel. The image is reconstructed after the refinement process. The green channel refinement algorithm includes the color difference is estimated as follows,

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(10)

At the red pixel, the green channel refinement includes the filter to obtain the color difference and it is given as follows,

(11)

By substituting R for B in the blue line, the green channel can be refined. For red channel prediction, the interpolated color difference and overlapping pixels are considered. The following formulas are used to achieve the red channel convergence phase in the green pixel:

(12)

Similarly, the blue component is estimated in the green pixel using the refinement process. The refinement of blue in red pixel and red in blue pixel are expressed below,

(13)

Substituting B as R in the above equation, the refinement of blue channel at red pixel is performed. The proposed algorithm combines the refined separate red, blue and green color planes to obtain the completely perfect coloured image. The advantages of refinement process are improving the quality image and reduction in color artifacts.

V. SIMULATIONS AND RESULTS The proposed algorithm is provided with low artifacts and has less computational complexity. This proposed work is experimentally tested using the programming language MATLAB. For this purpose, the 24 Kodak test images are considered, which are in TIFF format with size 512 x 768.

The performance of the proposed method is compared with existing systems namely edge-based algorithm [29], bilinear interpolation [24], and Alternative Projection [26]. And the performances are measured by the factors CPSNR and PSNR. Table1 shows the CPSNR comparison of the proposed algorithm with three different existing algorithms. The edge-based interpolation reduces the false color at the edge, but the quality of an image is low.

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Image Bilinear Edge based Alternative Proposed interpolation interpolation Projection Algorithm

1 30.773 31.2458 37.7 37.3460

2 36.6873 37.0425 39.57 39.2690

3 37.6775 38.0647 35.45 40.8823

4 37.4213 37.8047 40.03 40.5467

5 31.2157 31.8233 37.46 38.0349

6 32.1414 32.5964 38.5 37.7126

7 37.1797 37.6152 41.77 41.3229

8 28.2879 28.7457 42.02 34.3139

9 36.4896 36.9292 41.72 40.7968

10 36.3952 36.8716 42.02 40.9036

11 33.6645 34.1372 39.14 38.9460

12 36.9695 37.3912 42.51 40.7553

13 28.6113 29.1087 35.6 35.0059

14 33.469 33.9611 39.35 37.8504

15 35.4486 35.8213 39.35 39.1444

16 35.2366 35.6156 38.12 39.6689

17 36.681 37.2463 40.45 40.6713

18 32.562 33.0611 37.45 36.5432

19 32.7829 33.2797 36.07 39.0014

20 34.6422 34.9845 36.39 37.5616

21 33.0627 33.5363 38.66 37.7161

22 34.9277 35.3907 37.01 38.6690

23 38.3544 38.7374 39.45 43.0728

24 31.411 31.8746 34.78 35.1882

Table 1: Estimation of colour peak signal to noise ratio (CPSNR) performance in various methodologies.

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Performance measure: The considerations of demosaicing efficiency are CPSNR (color peak signal to noise ratio) and PSNR (peak signal to noise ratio). Most techniques in the image processing pipeline use CPSNR and PSNR calculations to evaluate their accuracy. The mean-square error (MSE) value is used in the color peak signal-to-noise ratio (CPSNR) calculation. It is calculated by taking the sum of the squared difference between the input image and the reconstructed image and then dividing it by the total size of the image.

The better performance of edge-oriented-filter depends on the magnitude of the MSE of the spectrums. The phase change in the frequency domain is due to the small spatial shift present in the image is visible to human eyes but still, there is a large difference in the MSE time-domain. The graph shows that the edge preservation capability is examined with an image in Bilinear, EP-Bilinear, SCB, and EP-SCP is shown in figure 4 and figure 5.

The formula for calculating the CPSNR is given as follow,

2552 I = 10log (14) CPSNR 10 I CMSE

I Where CMSE is a colour mean square error value of the image. And its expression is given as,

2 [I in (i, j) − I recons (i, j)] I CMSE =  (15) i, j 3* M * N

What happened to the original image? is a reflection of a picture that has been restored. The width and height of the image are expressed by M and N.

The picture with a higher CPSNR means that it is of higher quality, and the mean square error values are low. Images with a lower mean square error have better results.

PSNR (peak signal to noise ratio) is calculated as follows:

2552 I = 10log (16) PSNR 10 I MSE

I Here, the reconstructed image with a mean-square error (MSE) is denoted as MSE and it is expressed as follows,

2 [I in (i, j) − I recons (i, j)] I MSE =  (17) i, j M * N

The comparison table shows the CPSNR values of different interpolation techniques. The performance efficiency of proposed technique is high in comparison with the conventional system model performance.

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5 MSE of Frequency Domain

4.5 Bilinear EP Bilinear SCB EP-SCB 4

3.5

2.5

1.5 ERROR SQUARE -

1 MEAN 0.5

KODAK LOSSELESS TRUE COLOR IMAGE.

Figure 4: Frequency domain of Kodak true colour image.

250 MSE of Time Domain

Bilinear EP Bilinear SCB EP-SCB 200

150

100

SQUARE SQUARE ERROR -

50 MEAN

KODAK LOSSELESS TRUE COLOR IMAGE.

Figure 5: Frequency domain of Kodak true colour images.

VI. CONCLUSION This paper provides the edge-based interpolation technique with a refinement process. The original color and false color artifacts are commonly encountered in the demosaicing algorithm. The proposed technique produces low complexity, a high-quality image with reduced artifacts. The edge strength filter is used in the proposed system algorithm which utilizes edge description for the interpolation algorithm. Thus the performance efficiency of the proposed system model is increased using the Bayer components. Further, future research will focus on improvement in the demosaicing algorithm and the edge strength can be used in other image processing problems, it can reduce the artifacts and other problems. www.turkjphysiotherrehabil.org 992

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REFRENCES 1. R. Lukac and K. N. Plataniotis, “Color filter arrays: Design and performance analysis”, IEEE Trans Consumer Electron 51 (2005), 1260-1267. 2. P. Longere, X. Zhang, P. B. Delahunt, and D. H. Brainard, “Perceptual assessment of demosaicing algorithm performance”, Proc IEEE 90 (2002), 123-132. 3. B. K. Gunturk, J. Glotzbach, Y. Altunbasak, R. W. Schaffer, and R. M. Murserau, “Demosaicing: Color filter array interpolation”, IEEE Signal Process Mag 22 (2005) 44-54. 4. K. Parulski and K. E. Spaulding, “Color image processing for digital cameras”, in Digital Color Imaging Handbook, G. Sharma (Editor), CRC Press, Boca Raton, FL, 2002, pp. 728-757. 5. R. Ramanath, W. E. Snyder, Y. Yoo and M. S. Drew, “Color image processing pipeline”, IEEE Signal Processing Mag 22 (2005), 34-43. 6. Polesel, G. Ramponi, and V. J. Mathews, Image enhancement via adaptive unsharp masking”, IEEE Trans Image Process 19 (2000), 505-510. 7. M. Fischer, J. L. Paredes, and G. R. Arce, “Weighted median image sharpeners for the World Wide Web”, IEEE Trans Image Process 11 (2002), 717- 727. 8. Koschan and M. Abidi, “Detection and classification of edges in color images”, IEEE Signal Process Mag 22 (2005), 64-73. 9. R. Lukac, B. Smolka, K. Martin, K. N. Plataniotis, and A. N. Venetsanopulos, Vector filtering for color imaging, IEEE Signal Process Mag 22 (2005), 74-86. 10. Akshata Kulkarni, D. Rutuja Kurundkar, V. Shena Khare, Shubhashree Savant, Prashant Chintal, “A review on Color Image Processing”, Vol. 3 Issue. 11, November- 2015, pg. 5-10. 11. Ibrahim Pekkucuksen and Yucel Altunbasak, “Multiscale Gradients-Based Color Filter Array Interpolation”, IEEE Transactions On Image Processing, Vol. 22, No. 1, January 2013. 12. R. Lukac and K. N. Plataniotis, “Normalized Color-Ratio Modeling for CFA Interpolation” IEEE Transactions On Consumer 738 Electronics, Vol. 50, No. 2, May 2004. 13. Xin Li, “Demosaicing by Successive Approximation”, IEEE Transactions On Image Processing, Vol. 14, No. 3, March 2005. 14. Ibrahim Pekkucuksen, Yucel Altunbasak, “Edge oriented directional color filter array interpolation”, Center for Signal and Image Processing, Georgia Institute of Technology, Atlanta, GA, 30308, USA. 15. Daniele Menon, Stefano Andriani and Giancarlo Calvagno, “Demosaicing with Directional Filtering and a Posterior Decision”, IEEE Transactions On Image Processing, Vol. 16, No. 1, January 2007. 16. Wenmiao Lu and Yap-Peng Tan, “Color Filter Array Demosaicing: New Method and Performance Measures”, IEEE Transaction On Image Processing, Vol. 12, No. 10, October 2003. 17. Suresh, P., Saravanakumar, U., Celestine Iwendi, Senthilkumar Mohan, Gautam Srivastav 2020 “Field-programmable gate arrays with low power vision system using dynamic switching” Computers & Electrical Engineering, Vol. 90, 2021, 106996. 18. S. Matiacevich, D. Celis Cofre, P. Silva, J. Enroine, F. Osorio, “Quality Parameters of Six Cultivars of Blueberry Using Computer Vision”, International Journal of Food Science. 2013; 2013: 419535. Dio:10.1155/2013/419535. 19. Mery, Domingo, Franco Pedreschi, and Alvaro Soto, “Automated design of a computer vision system for visual food quality evaluation”, Food and Bioprocess Technology 6, no. 8 (2013): 2093-2108. DIO 10.1007/s11947-012-0934-2. 20. W. Yu., “Color demosaicing method using adaptive color convolution interpolation with sequential averaging,” in VISP, pp.666-676. 21. R. Woods R. Gonzalez and S. Eddins. Digital Image Processing Using MATLAB. Prentice-Hall, 2004. 22. X. Li, “Demosaicing by Successive Approximation, IEEE Transactions on Image Processing, vol. 14, no. 3, March 2005. 23. R. Kimmel, “Demosaicing: Image reconstruction from colorCCDsamples,” IEEE Trans. Image Process., vol. 8, no. 9, pp. 1221–1228, Sep.1999. 24. R. Lukac and K. N. Plataniotis, “A normalized model for color-ratio based demosaicing schemes,” in Int. Conf. on Image Process., 2004, vol. 3, pp. 1657–1660. 25. Chung-Yen Su, “Highly iterative Demosaicing using Weighted- Edge and Color-Difference Interpolations,” IEEE Transactions on Consumer Electronics, vol. 52, no. 2, pp. 639-646. 26. B. K. Gunturk, Y. Altunbasak, and R. M. Mersereau, “Color plane interpolation using alternating projections,” IEEE Trans. Image Process., vol. 11, no. 9, pp. 997–1013, Sep. 2002. 27. W. Lu and Y. P. Tang, “Color filter array demosaicing: New method and performance measures”, IEEE Trans. Image Processing, vol.12, no. 10, pp. 1194-1210, Oct. 2003. 28. R. Lukac, K. N. Plataniotis, D. Hatzinakos and M. Aleksic, “A novel cost effective demosaicing approach”, IEEE Trans. Consumer Electronics, vol. 50, no. 1, pp. 256-261, Feb. 2004. 29. I.Pekkucuksen, Y.Altunbasak, “Edge strength based color filter array interpolation,” IEEE Trans. Image Process, vol. 21, no. 1, pp. 393–397. 30. R. Lukac, K.N. Plataniotis, “Color filter array: performance analysis,” IEEE Trans. Cons. Elec., vol. 15, no. 4, pp. 1260-1208, Nov. 2005. 31. O. Lossona, L. Macairea, Y. Yanga, “Comparison of color demosaicing methods,” Advances in Imaging and Electron Physics, pp. 173-265. 32. N. Kehtarnavaz, H. J. Oh and Y. Yoo, “Color filter array interpolation using color correlations and directional derivatives,” Electronic Imaging Journal, vol. 12, no. 4, pp. 621-632, Oct. 2003. 33. J. E. Adams and J. F. Hamilton Jr., “Adaptive color plane interpolation in single color electronic camera,” U.S. Patent 5 506 619, Apr. 1996. 34. B. K. Gunturk, J. Glotzbach, Y. Altunbasak, R. W. Schafer and R. M. Mersereau, “Demosaicing: Color filter array interpolation,” IEEE Signal Processing Mag., vol. 22, no. 1, pp. 44-54, Jan. 2005. 35. W. T. Freeman, “Median filter for reconstructing missing color samples”, U. S. Patent 4724395, Feb. 9,1988. 36. S. C. Pei and I. K. Tam, “Effective color interpolation in ccd color filter arrays using signal correlation”, IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 6, pp. 503-513, Jun. 2003. 37. W. Lu and Y. P. Tan, “Color filter array demosaicing: New method and performance measures”, IEEE Trans. Image Processing, vol. 12, no. 10, pp. 1194-1210, oct. 2003.