Image Denoising by Utilizing Bilateral Filter Via Box Filtering Approach

IJRRAS 32 (3) ● September 2017 www.arpapress.com/Volumes/Vol32Issue3/IJRRAS_32_3_02.pdf

IMAGE DENOISING BY UTILIZING BILATERAL FILTER VIA BOX FILTERING APPROACH

Sheeraz Ahmed Solangi 1,*, Qunsheng Cao1 & Shumaila Solangi2 1 Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China 2 IMCS, University of Sindh, Jamshoro 76080, Pakistan

ABSTRACT Box and Gaussian filters are suitable and typically perform well in applications where amount of smoothing required is small. These types of filters are quite optimum in eliminating small amount of noise from natural images. However, when the floor of the noise is large, and one is essential to average more pixels to suppress the noise, so these type of filters begin to over smooth sharp image features such as corners and edges. To overcome this shortcoming the bilateral filter has been proposed. In this paper we have presented new approach by utilizing bilateral filter via box filter method. The proposed method significantly improve the bilateral filter performance by simply implementing pre- processing step of box filtering at almost no additional cost.

Keywords: Bilateral filtering, Box filtering, Image denoising, Edge-preserving

1. INTRODUCTION The bilateral filter (BF) is a kind of non-linear weighted averaging filter [1]. The methodology of bilateral filter is that the weights of the filter is totally depend on the spatial distance and the photometric similarity distance with respect to the center pixel. While doing the spatial smoothing the bilateral filter have the capability to preserve the edges that is the main characteristic of this filter. The performance of bilateral filter is relatively effective in denoising images degraded with small amounts of Gaussian noise. The bilateral filter perform very well in denoising images, though, images degrade quickly as increase in noise level. To overcome this shortcoming of the filter there are a lot of modified versions of the bilateral filter have been suggested in the literature, but often at an extensive computational overhead.

There are wide range of applications in computer vision and image processing has found for bilateral filter. The prompt application of bilateral filter is as it can do spatial averaging in images without smoothing edges. Other applications of the bilateral filter comprise of image denoising multiresolution addition [2], contrast enhancement [3], 3-D mesh denoising [4], estimation of depth map [5], illumination and texture separation [6], tone mapping [7], enhancement of video [8], compression artifact reduction [9], estimation of optical flow [10] and fusion of flash and no-flash images [11].

In this paper we have utilized bilateral filter that can significantly improve the bilateral filter performance by implementing simple pre-processing step. There is no additional cost of this filter. The performance of the filter is significant at high noise levels as compare to the existing bilateral filter but at certain noise threshold it tends to perform poor. Both the numerical and visual denoising results on standard test images have been reported in this paper. In the experimental results, we have verified that, by utilizing bilateral filter via box filter the performance over the existing bilateral filter is significant both visually and numerically in terms of PSNR. Additionally, the denoising performance of the proposed filter is competitive with the anisotropic diffusion (AD) [12], total variation (TV) [13] and non-local means filter (NLM) [14] in terms of visual and computational process.

2. STANDARD PROBLEM OF DENOISING The images degraded with the white Gaussian noise that is the standard problem of denoising grayscale images in image processing and computer vision [15], [13], [16]. In this method, we are given the noisy image as:

푢(풊) = 푢°(풊) + 휎 ∙ 푤푖 (풊 ∈ 퐾) (1)

2 where K is finite rectangular domain of ℤ , { 푢°(풊) ∶ 풊 ∈ 퐾 } is the unknown original clean image, {푤푖: 풊 ∈ 퐾 } are independent and identically distributed as N (0, 1), and the noise level is σ. The main aim is to discover an estimate denoised 푢̂(풊) of the original clean image from the degraded samples. So the goal is to denoised an image that should be visually similar to original clean image. By using mean-squared-error (MSE) we can calculate the resemblance rate of image which is defined to be:

IJRRAS 32 (3) ● September 2017 Solangi et al. ● Image Denoising by Utilizing Bilateral Filter

1 MSE = ∑( 푢̂ (풊) − 푢 (풊))ퟐ |퐾| ° (2) 풊∈퐾

2 So, in this paper, we have used the peak signal-to-noise ratio (PSNR) which is given by PSNR = 10log10(255 /MSE).

Edge-preserving bilateral filter in the applications of image denoising is the current interest in our paper [1], [17], [18]. In this case we have designed the denoised image as:

Σ풋єΩ 𝑔휎푠(풋) 𝑔휎푟(푢(풊 − 풋) − 푢(풊)) 푢(풊 − 풋) 푢̂1(풊) = (3) Σ풋єΩ 𝑔휎푠(풋) 𝑔휎푟(푢(풊 − 풋) − 푢(풊)) where,

‖푖‖2 푡2 𝑔휎푠(푖) = exp (− 2 ) and 𝑔휎푟 (푡) = exp (− 2 ) (4) 2휎푠 2휎푟

The spatial and range kernels are given in Eq. (4) which refers for Gaussian kernels. The domain Ω, in practice is limited to some neighborhood of the origin. Usually, the square of the neighborhood is Ω, which refers to Ω = [-W;

W] × [-W; W], where W = 3휎푠[1].

3. BOX FILTERING APPROACH A box filter is kind of spatial averaging filter in which all coefficients are must be equal. Spatial averaging filters are mostly used for smoothing and for image denoising. The output of the each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input.

The idea behind spatial averaging filters is straight forward. By replacing every pixel value in an image by the mean of the intensity levels in the neighborhood of the filter mask. So, in this method the results in an image with reduced ‘sharp changes’ in intensity levels. Hence, the noise reduction is the most noticeable application of smoothing. The main drawback of these filters is they blur edges of the image.

Box filtering in its simplest form can be described as [19]:

1 ℎ퐾퐿 = (5) 푘푙 (2퐾 + 1)(2퐿 + 1)

퐾퐿 퐾퐿 where, ℎ with real samples ℎ푘푙 .

4. UTILIZING BILATERAL FILTER VIA BOX FILTERING We have demonstrated in this section that how bilateral filter denoising performance can be improved at high noise levels. So, by integrating a simple pre-processing step into the framework at almost no additional cost. We have noted that in Eq. (3) the range filter operates on the noisy samples.

It means that the degraded image is used not just for averaging but also to control the smoothing via range filter. The approach in this regard is to apply range filter directly on the original clean image so it can consider some form of proxy for the original clean image. In this scheme we use the box filter as a proxy for the noisy image. In particular, proposed bilateral filter can be written as:

Σ풋єΩ 𝑔휎푠(풋) 𝑔휎푟 (푢̅(풊 − 풋) − 푢̅(풊)) 푢(풊 − 풋) 푢̂2(풊) = (6) Σ풋єΩ 𝑔휎푠(풋) 𝑔휎푟 (푢̅(풊 − 풋) − 푢̅(풊)) where,

1 푢̅(푖) ∑ 푢(푖 − 푗) (2푅 + 1)2 (7) 푗∈{−푅,푅}2

IJRRAS 32 (3) ● September 2017 Solangi et al. ● Image Denoising by Utilizing Bilateral Filter

We have set the parameter R in which can we can control the amount of smoothing induced by the box filter. We have performed number of simulations in this regard. The number of simulation results recommend that R=1 (3 × 3) is ideal for most of output results. By doing this we can explain this as the small blur can suppress the noise but also can keep features of the image. We have performed denoising on both original bilateral filter and proposed method and obtained results of test images are shown in Table 1. So the results of these filters can be tuned independently with respect to 휎푠 and 휎푟. The tuning of these filters can get the best PNSR results.

4. EXPERIMENTAL RESULTS We have used MATLAB R2013a (8.1), CPU: 1.70GHz, Memory: 4GB and Windows 10 platform and performed simulations for anisotropic diffusion (AD), total variation (TV), bilateral filter (BF) and proposed method. We have taken standard test images of Lena, Pepper, Goldhill and Barbara of dimension of 512×512 for simulation purpose. These images are degraded with Gaussian noise of standard deviation 휎n = 10, 20, 30, 40, and 50. The execution time and PSNR are taken as performance measures.

In Table 1 various images of PNSR values for different filters and proposed filter are given. In this we have highlighted the best PNSR value for these various images as given in Table 1.

The visual results shown in figures: Figure1, Figure 2, Figure 3 and Figure 4. It is suggest that beyond a certain noise level depending on the type of image the proposed filter starts to perform better. Adding up the box filtering into the bilateral filter can perform better at high noise levels but brings down the overall PSNR due to the blurring of the image. Certainly, the degraded image is already a good proxy for the original clean image when the noise ratio is small. Moreover, we have noted that the improvement in peak-signal-to-noise-ratio is often as large as 8 dB at high noise levels as compare to the original bilateral filter.

In Table 2 is execution time of these filters are given. For online and real time applications the less execution time filter usually preferred. The minimum execution time is highlighted in this text. Hence, this filter is powerful tool used in today’s applications.

Original Image Noisy Image, PSNR = 18.60 Noisy Image, PSNR = 28.13

(a) Original Lena image (b) Noisy image (c) Denoised by AD

Denoised image Original Bilateral Filter, PSNR = 24.79 Improved BF, PSNR = 29.50

(d) Denoised by TV (e) DenoisedImprovement by = BF6.1908 (f) Proposed method

Figure 1. Denoising performance of various filters and proposed method for Lena test image with noise = 30.

IJRRAS 32 (3) ● September 2017 Solangi et al. ● Image Denoising by Utilizing Bilateral Filter

Original Image Noisy Image, PSNR = 14.15 Noisy Image, PSNR = 18.59

(a) Original Lena image (b) Noisy image (c) Denoised by AD

Denoised image Original Bilateral Filter, PSNR = 18.37 Improved BF, PSNR = 27.47

(d) Denoised by TV (e) DenoisedImprovement by =BF 4.2152 (f) Proposed method

Figure 2. Denoising performance of various filters andNoisy Image,proposed PSNR = 22.11 method for Lena test imageNoisy Image, PSNR with = 28.13 noise = 50.

(a) Original Goldhill image (b) Noisy image (c) Denoised by AD

Denoised image Standard Bilateral Filter, PSNR = 27.74 Improved BF, PSNR = 28.86

(d) Denoised by TV (e) Denoised by BF (f) Proposed method

Figure 3. Denoising performance of various filters and proposed method for Goldhill test image with noise = 20.

IJRRAS 32 (3) ● September 2017 Solangi et al. ● Image Denoising by Utilizing Bilateral Filter

Noisy Image, PSNR = 18.62 Noisy Image, PSNR = 22.11

(a) Original Pepper image (b) Noisy image (c) Denoised by AD

Denoised image Original Bilateral Filter, PSNR = 25.43 Improved BF, PSNR = 30.03

(d) Denoised by TV (e) DenoisedImprovement by = 6.8442 BF (f) Proposed method

Figure 4. Denoising performance of various filters and proposed method for Peppers test image with noise = 30.

IJRRAS 32 (3) ● September 2017 Solangi et al. ● Image Denoising by Utilizing Bilateral Filter

Table 1. Filtering performance of various filters and proposed method in terms of PSNR (dB) operated on different test images under various noise conditions (휎푛 varies from 10 to 50).

Peak-Signal-To-Noise-Ratio, PSNR (dB)

Denoising Standard deviation of noise Test Images Filters 10 20 30 40 50

AD 31.36 29.26 27.21 25.39 23.92

TV 33.23 30.25 27.84 25.86 24.11 Lena BF 33.53 28.02 24.79 22.37 18.37

Proposed 33.28 30.90 29.50 28.44 27.47

AD 30.67 28.80 26.84 25.10 23.62

TV 31.20 29.09 27.11 25.30 23.75 Pepper BF 33.70 28.73 25.43 20.71 18.79

Proposed 33.14 31.64 30.03 28.28 27.04

AD 29.87 28.26 26.49 24.82 23.39

TV 31.29 29.12 27.19 25.42 23.77 Goldhill BF 31.83 27.74 25.23 22.65 21.52

Proposed 30.41 28.86 27.70 26.99 26.16

AD 26.05 25.34 24.36 23.31 22.27

TV 26.46 25.47 24.41 23.32 22.30 Barbara BF 31.20 27.05 24.12 22.02 20.75

Proposed 26.06 25.07 24.19 23.76 23.27

IJRRAS 32 (3) ● September 2017 Solangi et al. ● Image Denoising by Utilizing Bilateral Filter

Table 2. Filtering performance of various filters and proposed method in terms of PSNR (dB) operated on different test images under various noise conditions (휎푛 varies from 10 to 50).

Execution time (푇퐸), (seconds) Denoising Filters Lena Pepper Goldhill Barbara

AD 6.55 6.77 7.01 7.21

TV 9.26 9.61 9.33 8.66

BF 4.31 3.73 4.73 5.26

Proposed method 3.11 3.42 3.18 3.85

5. CONCLUSION The performance of AD, TV, BF and proposed method are studied under low, moderate and high noise conditions. From Table 1, it is observed that the proposed method perform better in terms of PSNR as compare to the original bilateral filter and also competitve with the other filters depending on the type of image. As increasing of noise in these filters the performance of filters reduces in terms of PNSR and as well as makes image more blurry. The proposed filter perform quite well and preserve the edges in low and high noise conditions. The original bilateral filter perform well in low noise conditions only. The execution time of these filters is shown in the Table 2 which suggests the execution time of proposed method is better than the other filters.

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