Intelligent and Non-Intelligent Approaches in Image Denoising: A Comparative Study

2014 ◽  
pp. 125-160
Author(s):  
Mantosh Biswas ◽  
Hari Om
Keyword(s):  
Comparative Study ◽  
Image Denoising

scholarly journals Improved Image Denoising Methodology using Deep CNN Bilateral Filter Compared to Additional Methods

2021 ◽  
Vol 10 (5) ◽  
pp. 191-196
Author(s):  
Pallavi Bora ◽  
Kapil Chaudhary
Keyword(s):  
Standard Deviation ◽  
Comparative Study ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Bilateral Filter ◽  
Filter Method ◽  
Novel Approach ◽  
Deep Cnn ◽  
Test Result

Image Denoising techniques are widely used to remove the noise from the images. Due to the ease of the bilateral filter, it is used very often to remove the noise from the images. In this paper, a novel approach has been proposed to enhanced bilateral filter in conjunction with CNN as a booster to eliminate Gaussian noise from Grey images. Studies reveal that standard CNN using a bilateral filter is the best technique to eliminate Gaussian noise from images along with high PSNR values. This paper also performs a comparative study of the various existing techniques for image denoising with the CNN technique and the applied Bilateral filter Method as a de facto to improve the results in terms of enhanced PSNR values. ECND Net (Enhanced CNN) applied to noisy images with standard deviation σ = 15 gives PSNR values up to 32.81 In comparison to this when both bilateral filter and deep CNN applied, in conjunction produces improved PSNR values up to 34.73 along with the equivalent standard deviation. The results in this work reveal better performance in terms of PSNR as compared to other methods. The test result proves that the bilateral filter Method along with CNN can improve the quality of restored images significantly better.

scholarly journals A Comparative Study between X_Lets Family for Image Denoising

2014 ◽  
Vol 6 (3) ◽  
pp. 43-50
Author(s):  
Beladgham Mohamed ◽  
Habchi Yassine ◽  
Moulay Lakhdar Abdelmouneim ◽  
Abdesselam Bassou ◽  
Taleb Ahmed Abdelmalik
Keyword(s):  
Comparative Study ◽  
Image Denoising

scholarly journals A COMPARATIVE STUDY OF VARIOUS WAVELET APPROACHES USED IN IMAGE DENOISING

2021 ◽  
Vol 9 (1) ◽  
pp. 1061-1078
Author(s):  
Vijayaragahvan Veeramani, Laavanya Mohan
Keyword(s):  
Comparative Study ◽  
Image Denoising ◽  
Digital Image ◽  
Gaussian Noise ◽  
Environmental Changes ◽  
Vital Role ◽  
Visual Appearance ◽  
Fine Grain ◽  
Definitive Solution ◽  
Changes Over Time

The world is constantly changing, and vision helps the humans to understand the environmental changes over time. The changes can be seen by, capturing the images. Hence digital image plays a vital role in day to day life. During the process of acquisition of digital image, the qualities of digital pictures are degraded due to additive noise known as adaptive white Gaussian noise. Therefore, the major challenge of image denoising algorithm is to improve the visual appearance while preserving the other details of the image. For the last two decades, wavelet has become an elegant tool in image denoising techniques. Among all wavelet based denoising methods, wavelet thresholding became popular because, wavelet appropriately separates the noisy signal from the image. The wavelet separation leaves the coarse grain noise in approximation sub-band and fine grain noise in detail sub-bands. Therefore, in wavelet based thresholding methods noise in detail sub-bands are threshold and approximate sub-band noise are kept as such. Hence, the efficiency of all wavelet based shrinkage techniques depends on, the choice of threshold parameter, thresholding technique and how the noise in the approximation sub-bands are handled. This paper presents a brief comparative study of denoising techniques proposed in the research articles based on the above parameters for Gaussian noise reduction using various wavelets transform. With the help of these experiments, we are able to identify the strengths and weaknesses of these methods, as well as seek the way ahead towards a definitive solution to the long-standing problem of image denoising.  

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