scholarly journals A Novel Approach for Super Resolution of Video Frame using Spatially Adaptive Total Variation

2017 ◽  
Vol 176 (4) ◽  
pp. 29-34
Author(s):  
Vinod Kumar ◽  
Kamlesh Chandravanshi
Keyword(s):  
Total Variation ◽  
Super Resolution ◽  
Video Frame ◽  
Spatially Adaptive ◽  
Novel Approach ◽  
Adaptive Total Variation

scholarly journals Spatially Adaptive Tensor Total Variation-Tikhonov Model for Depth Image Super Resolution

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 13857-13867 ◽  
Author(s):  
Gang Zhong ◽  
Sen Xiang ◽  
Peng Zhou ◽  
Li Yu
Keyword(s):  
Total Variation ◽  
Super Resolution ◽  
Depth Image ◽  
Spatially Adaptive ◽  
Image Super Resolution

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 Efficient and Effective Total Variation Image Super-Resolution: A Preconditioned Operator Splitting Approach

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Li-Li Huang ◽  
Liang Xiao ◽  
Zhi-Hui Wei
Keyword(s):  
Total Variation ◽  
Operator Splitting ◽  
Super Resolution ◽  
Initial Guess ◽  
Iteration Algorithm ◽  
Splitting Technique ◽  
Global Rate ◽  
Novel Approach ◽  
Image Super Resolution ◽  
Thresholding Algorithm

Super-resolution is a fusion process for reconstructing a high-resolution image from a set of low-resolution images. This paper proposes a novel approach to image super-resolution based on total variation (TV) regularization. We applied the Douglas-Rachford splitting technique to the constrained TV-based variational SR model which is separated into three subproblems that are easy to solve. Then, we derive an efficient and effective iterative scheme, which includes a fast iterative shrinkage/thresholding algorithm for denoising problem, a very simple noniterative algorithm for fusion part, and linear equation systems for deblurring process. Moreover, to speed up convergence, we provide an accelerated scheme based on precondition design of initial guess and forward-backward splitting technique which yields linear systems of equations with a nice structure. The proposed algorithm shares a remarkable simplicity together with a proven global rate of convergence which is significantly better than currently known lagged diffusivity fixed point iteration algorithm and fast decoupling algorithm by exploiting the alternating minimizing approach. Experimental results are presented to illustrate the effectiveness of the proposed algorithm.

scholarly journals Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yaduan Ruan ◽  
Houzhang Fang ◽  
Qimei Chen
Keyword(s):  
Total Variation ◽  
Spatial Information ◽  
Total Variation Regularization ◽  
Image Deconvolution ◽  
Split Bregman ◽  
Edge Information ◽  
Spatially Adaptive ◽  
Comparative Results ◽  
Simulated Images ◽  
Adaptive Total Variation

A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported.

Spatially adaptive total variation deblurring with split Bregman technique

2018 ◽  
Vol 12 (6) ◽  
pp. 948-958 ◽  
Author(s):  
Mahdi Dodangeh ◽  
Isabel N. Figueiredo ◽  
Gil Gonçalves
Keyword(s):  
Total Variation ◽  
Split Bregman ◽  
Spatially Adaptive ◽  
Adaptive Total Variation
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