scholarly journals Adaptive Total Variation Minimization-Based Image Enhancement from Flash and No-Flash Pairs

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Sang Min Yoon ◽  
Yeon Ju Lee ◽  
Gang-Joon Yoon ◽  
Jungho Yoon
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Total Variation ◽  
Main Idea ◽  
Numerical Results ◽  
Ambient Light ◽  
Total Variation Minimization ◽  
Novel Approach ◽  
Adaptive Total Variation

We present a novel approach for enhancing the quality of an image captured from a pair of flash and no-flash images. The main idea for image enhancement is to generate a new image by combining the ambient light of the no-flash image and the details of the flash image. In this approach, we propose a method based on Adaptive Total Variation Minimization (ATVM) so that it has an efficient image denoising effect by preserving strong gradients of the flash image. Some numerical results are presented to demonstrate the effectiveness of the proposed scheme.

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 Total Variation Wavelet-Based Medical Image Denoising

2006 ◽  
Vol 2006 ◽  
pp. 1-6 ◽  
Author(s):  
Yang Wang ◽  
Haomin Zhou
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Medical Image ◽  
Medical Images ◽  
Stopping Time ◽  
Noise Removal ◽  
Total Variation Minimization ◽  
Time Criterion ◽  
Minimization Scheme

We propose a denoising algorithm for medical images based on a combination of the total variation minimization scheme and the wavelet scheme. We show that our scheme offers effective noise removal in real noisy medical images while maintaining sharpness of objects. More importantly, this scheme allows us to implement an effective automatic stopping time criterion.

scholarly journals An Image Enhancement Algorithm Based on Fractional-Order Phase Stretch Transform and Relative Total Variation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Wang ◽  
Ying Jia ◽  
Qiming Wang ◽  
Pengfei Xu
Keyword(s):  
Image Enhancement ◽  
Total Variation ◽  
Fractional Order ◽  
Complex Environments ◽  
Low Contrast ◽  
Low Pass ◽  
Phase Stretch Transform ◽  
Enhancement Algorithm ◽  
Relative Total Variation

The main purpose of image enhancement technology is to improve the quality of the image to better assist those activities of daily life that are widely dependent on it like healthcare, industries, education, and surveillance. Due to the influence of complex environments, there are risks of insufficient detail and low contrast in some images. Existing enhancement algorithms are prone to overexposure and improper detail processing. This paper attempts to improve the treatment effect of Phase Stretch Transform (PST) on the information of low and medium frequencies. For this purpose, an image enhancement algorithm on the basis of fractional-order PST and relative total variation (FOPSTRTV) is developed to address the task. In this algorithm, the noise in the original image is removed by low-pass filtering, the edges of images are extracted by fractional-order PST, and then the images are fused with extracted edges through RTV. Finally, extensive experiments were used to verify the effect of the proposed algorithm with different datasets.

scholarly journals 3D Wavelet Subbands Mixing for Image Denoising

2008 ◽  
Vol 2008 ◽  
pp. 1-11 ◽  
Author(s):  
Pierrick Coupé ◽  
Pierre Hellier ◽  
Sylvain Prima ◽  
Charles Kervrann ◽  
Christian Barillot
Keyword(s):  
Image Denoising ◽  
Computation Time ◽  
Real Data ◽  
Critical Issue ◽  
Noise Removal ◽  
Total Variation Minimization ◽  
Quantitative Validation ◽  
Fully Automatic ◽  
Synthetic Datasets

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. The method proposed in this paper is a fully automatic 3D blockwise version of the nonlocal (NL) means filter with wavelet subbands mixing. The proposed wavelet subbands mixing is based on a multiresolution approach for improving the quality of image denoising filter. Quantitative validation was carried out on synthetic datasets generated with the BrainWeb simulator. The results show that our NL-means filter with wavelet subbands mixing outperforms the classical implementation of the NL-means filter in terms of denoising quality and computation time. Comparison with wellestablished methods, such as nonlinear diffusion filter and total variation minimization, shows that the proposed NL-means filter produces better denoising results. Finally, qualitative results on real data are presented.

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