Degraded image restoration based on quadtree decomposition in scattering media

2020 ◽  
Author(s):  
Yingbo Wang ◽  
Jie Cao ◽  
Chengqiang Xu ◽  
Chenyu Xu ◽  
Qun Hao
Keyword(s):  
Image Restoration ◽  
Scattering Media ◽  
Quadtree Decomposition ◽  
Degraded Image

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

2D Wavelet Tree Ordering Based Localized Total Variation Model for Efficient Image Restoration

2022 ◽  
Author(s):  
K. Praveen Kumar ◽  
C. Venkata Narasimhulu ◽  
K. Satya Prasad
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Good Condition ◽  
Window Size ◽  
Local Variation ◽  
Image Simulation ◽  
Regularization Algorithm ◽  
Total Variation Model ◽  
Degraded Image ◽  
Adjustment Scheme

The degraded image during the process of image analysis needs more number of iterations to restore it. These iterations take long waiting time and slow scanning, resulting in inefficient image restoration. A few numbers of measurements are enough to recuperate an image with good condition. Due to tree sparsity, a 2D wavelet tree reduces the number of coefficients and iterations to restore the degraded image. All the wavelet coefficients are extracted with overlaps as low and high sub-band space and ordered them such that they are decomposed in the tree ordering structured path. Some articles have addressed the problems with tree sparsity and total variation (TV), but few authors endorsed the benefits of tree sparsity. In this paper, a spatial variation regularization algorithm based on tree order is implemented to change the window size and variation estimators to reduce the loss of image information and to solve the problem of image smoothing operation. The acceptance rate of the tree-structured path relies on local variation estimators to regularize the performance parameters and update them to restore the image. For this, the Localized Total Variation (LTV) method is proposed and implemented on a 2D wavelet tree ordering structured path based on the proposed image smooth adjustment scheme. In the end, a reliable reordering algorithm proposed to reorder the set of pixels and to increase the reliability of the restored image. Simulation results clearly show that the proposed method improved the performance compared to existing methods of image restoration.

Progress of degraded image restoration methods

2018 ◽  
Vol 33 (8) ◽  
pp. 676-689
Author(s):  
李俊山 LI Jun-shan ◽  
杨亚威 YANG Ya-wei ◽  
张姣 ZHANG Jiao ◽  
李建军 LI Jian-jun
Keyword(s):  
Image Restoration ◽  
Degraded Image

RL turbulence degraded image restoration algorithm based on wavelet transform

2017 ◽  
Vol 32 (10) ◽  
pp. 822-827
Author(s):  
徐晓睿 XU Xiao-rui ◽  
戴 明 DAI Ming ◽  
尹传历 YIN Chuan-li
Keyword(s):  
Wavelet Transform ◽  
Image Restoration ◽  
Restoration Algorithm ◽  
Degraded Image

An Adaptive Variational PDE Based Image Restoration Model Using Hopfield Neural Network

2011 ◽  
Vol 48-49 ◽  
pp. 174-178
Author(s):  
Wei Sun ◽  
Sheng Nan Liu
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Adaptive Regularization ◽  
Proposed Model ◽  
Degraded Image ◽  
Restoration Problem ◽  
Restoration Model

An adaptive variational partial differential equation (PDE) based aproach for restoration of gray level images degraded by a known shift-invariant blur function and additive noise is presented. The restoration problem of a degraded image is solved by minimizing this model, and this minimizing problem is realized by using Hopfield neural network. In the proposed image restoration model, an adaptive regularization parameter is developed instead of the constant regularization parameter used in previous PDE model. The value of the adaptive regularization parameter changes according to different regions of the image to remove noises and preserve edge better. Several computer simulation results show that the image restoration results of the proposed model both look better and have better SNR (Signal to Noise Ratio) than the previous variational PDE based model.

Optimum Color and Contrast Enhancement for Online Ferrography Image Restoration

2019 ◽  
Vol 2 (3) ◽  
Author(s):  
Lingfeng Yang ◽  
Tonghai Wu ◽  
Kunpeng Wang ◽  
Hongkun Wu ◽  
Ngaiming Kwok
Keyword(s):  
Image Restoration ◽  
Contrast Enhancement ◽  
Real Time ◽  
Lagrange Multiplier ◽  
Information Loss ◽  
Cost Functions ◽  
High Quality ◽  
Degraded Image ◽  
Restoration Method ◽  
The Cost

Online ferrography, because of its nondestructive and real-time capability, has been increasingly applied in monitoring machine wear states. However, online ferrography images are usually degraded as a result of undesirable image acquisition conditions, which eventually lead to inaccurate identifications. A restoration method focusing on color correction and contrast enhancement is developed to provide high-quality images for subsequent processing. Based on the formation of a degraded image, a model describing the degradation is constructed. Then, cost functions consisting of colorfulness, contrast, and information loss are formulated. An optimal restored image is obtained by minimizing the cost functions, in which parameters are properly determined using the Lagrange multiplier. Experiments are carried out on a collection of online ferrography images, and results show that the proposed method can effectively improve the image both qualitatively and quantitatively.

scholarly journals Image Restoration using Mirroring Method Which Based on the Gradient Direction

2020 ◽  
Vol 188 ◽  
pp. 00011
Author(s):  
I Komang Somawirata ◽  
Aryuanto Soetedjo ◽  
Sotyohadi Sotyohadi ◽  
Fitri Utaminingrum ◽  
Maizirwan Mel
Keyword(s):  
Image Restoration ◽  
Previous Method ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Gradient Direction ◽  
Degraded Image ◽  
Image Area

Restoring a damaged image is a challenging topic in the field of image restoration. The famous previous method for restoring a degraded image are filters (inverse and wiener) and maximum a posteriori (MAP) formulation. However, that method has limited performance for restoring damaged images. In this paper, the multi mirroring method have been implemented for reconstructing damaged image which based on gradient direction. Firstly, the method will detect damaged image areas and then the multi mirroring method is implemented for filling a damaged image area. The simulation result shows that the proposed method has good result and capable to restore the damaged image.

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