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).