Adaptive Proximal Point Algorithms for Total Variation Image Restoration

Chen, Ying; Wu, Jian; Yu, Gaohang

doi:10.19139/122

Cited by 5 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Image processing is generally used for estimating motion and shift between sub sequent images of the video or time deleted images [7]. There are lists of traditional restoration algorithms [8][9], However they are not useful for mitigation of atmospheric degradation. In image registration, sequential video frames are used for computing the time-varying distortion by exploiting quasi-periodicities in a motion field.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Image Registration Technique to Remove Atmospheric Turbulence

Patel¹,

Israni

Kumar³

et al. 2019

Stat., optim. inf. comput.

View full text Add to dashboard Cite

Turbulence/Heat Scintillations is the change that arises in the refraction index of air with the temperature. The distortion created by the atmospheric turbulence is proportional to the distance between object and camera. In last few years, several approaches have been proposed to estimate and eliminate geometric distortion and blur. In this paper, a novel technique is proposed that improves the visual quality of video sequences affected by atmospheric turbulence. The proposed method is based on adaptive control grid interpolation (CGI). CGI approximates accurate motion vectors to generate a geometrically correct frame using certain reference frames. For high scintillation sequences, CGI doesn ′ t mitigate scintillations completely. The new methodology is proposed with updated trajectory estimation. The proposed method can effectively reduce the influence even for high atmospheric turbulence. Experimental results also prove that proposed approach is time efficient compare to traditional CGI.

show abstract

Section: Related Workmentioning

confidence: 99%

An Adaptive Image Registration Technique to Remove Atmospheric Turbulence

Patel¹,

Israni

Kumar³

et al. 2019

Stat., optim. inf. comput.

View full text Add to dashboard Cite

show abstract

“…According to Kolmogorov's turbulence theory, the statistical short exposure optic transfer function (OTF) of ground based telescope imaging systems is described as [6]:…”

Section: An Atmospheric Turbulence-degraded Modelmentioning

confidence: 99%

A Turbulence-Degraded Image Restoration Algorithm Using the Maximum Likelihood Estimation Method

Wang

Zhang

Meng

et al. 2014

AMM

View full text Add to dashboard Cite

The RL(Richardson-Lucy) algorithm is an important method for restoration of turbulence-degraded images. However, the shortcoming of this method is that it tends to amplify the noise and exsits excessive smoothing in the iterative procedure. This paper discusses the RL algorithm and its improving methods focusing on turbulence-degraded images restoration.Firstly, a short exposure atmospheric turbulence-degraded model is established and a numerical computing method is proposed for random phase screen. Secondly, the essential principle and computational formula are deduced. To restore the object image effectively from the turbulence-degraded image, a new double-circulation iterative Richardson-Lucy restoration algorithm using TV-regularized method is proposed. This new algorithm introduces the total variation restraint and estimates the object image and the point spread function based on the inner and outer double-circulation iteration, which can use the inherent relation between the object image and the point spread function adequately. Simulation experiments show that the proposed algorithm can effectively preserve the details and edges of the image and its restoration effect is obviously better than the traditional RL algorithm.

show abstract

“…In the industry, image restoration is an important topic. This is a fundamental problem in a variety of applied sciences, including medical imaging [1] [2], microscopy and astronomy [3], film restoration, and image and video coding [4] [5]. Image restoration is an interesting subject in image processing since it occurs at the very beginning of the acquisition chain and involves recovering a clean original image from a degraded image [3] [2].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Prox Penalty and Bregman Algorithms for Image Denoising

Bougueroua,

Daili

2023

Journal of Applied Mathematics

View full text Add to dashboard Cite

Image restoration is an interesting ill-posed problem. It plays a critical role in the concept of image processing. We are looking for an image that is as near to the original as possible among images that have been skewed by Gaussian and additive noise. Image deconstruction is a technique for restoring a noisy image after it has been captured. The numerical results achieved by the prox-penalty method and the split Bregman algorithm for anisotropic and isotropic TV denoising problems in terms of image quality, convergence, and signal noise rate (SNR) are compared in this paper. It should be mentioned that isotropic TV denoising is faster than anisotropic. Experimental results indicate that the prox algorithm produces the best high-quality output (clean, not smooth, and textures are preserved). In particular, we obtained (21.4, 21) the SNR of the denoising image by the prox for sigma 0.08 and 0.501, such as we obtained (10.0884, 10.1155) the SNR of the denoising image by the anisotropic TV and the isotropic TV for sigma 0.08 and (-1.4635, -1.4733) for sigma 0.501.

show abstract

Adaptive Proximal Point Algorithms for Total Variation Image Restoration

Cited by 5 publications

References 21 publications

An Adaptive Image Registration Technique to Remove Atmospheric Turbulence

An Adaptive Image Registration Technique to Remove Atmospheric Turbulence

A Turbulence-Degraded Image Restoration Algorithm Using the Maximum Likelihood Estimation Method

Comparative Analysis of the Prox Penalty and Bregman Algorithms for Image Denoising

Contact Info

Product

Resources

About