Blind restoration of real turbulence-degraded image with complicated backgrounds using anisotropic regularization

2012 ◽  
Vol 285 (24) ◽  
pp. 4977-4986 ◽  
Author(s):  
Hanyu Hong ◽  
Liangcheng Li ◽  
Tianxu Zhang
Keyword(s):  
Degraded Image ◽  
Blind Restoration ◽  
Anisotropic Regularization

A Blur Classification Approach Using Deep Convolution Neural Network

2020 ◽  
Vol 11 (1) ◽  
pp. 93-111
Author(s):  
Shamik Tiwari
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Hand Gesture ◽  
Classification Approach ◽  
Degraded Image ◽  
Blind Restoration ◽  
Deep Convolution Neural Network ◽  
Simulation Results ◽  
Mlp Model ◽  
Degraded Images

Computer vision-based gesture identification is designed to recognize human actions with the help of images. During the process of gesture image acquisition, images suffer various degradations. The method of recovering these degraded images is called restoration. In the case of blind restoration of such a degraded image where blur information is unavailable, it is essential to determine the exact blur type. This article presents a convolution neural network model for blur classification which categories a blur found in a hand gesture image into one of the four blur categories: motion, defocus, Gaussian, and box blur. The simulation results demonstrate the improved preciseness of the CNN model when compared to the MLP model.

scholarly journals Estimating the Size of the Point Spread Function Using Fuzzy Logic for Blind Restoration of Satellite Image

2017 ◽  
Vol 5 (4) ◽  
pp. 48
Author(s):  
Fouad Aouinti ◽  
M'barek Nasri ◽  
Mimoun Moussaoui
Keyword(s):  
Point Spread Function ◽  
Fuzzy Inference ◽  
Blind Deconvolution ◽  
Satellite Image ◽  
Inference System ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
Blind Restoration ◽  
Image Edges

Despite the considerable progress in the field of imaging, the acquired image can undergo certain degradations which are mainly summarized in blur and noise. The objective of the restoration is to estimate from the observed image an image as close as possible to the original image. The iterative blind deconvolution (IBD) can be used effectively when no information about the distortion is known. This algorithm starts with a random initial estimate of the point spread function (PSF) whose its size affects strongly the restoration process of the degraded image. In this paper, we have implemented a fuzzy inference system (FIS) to determine the size of the PSF through the examination of the blurred satellite image edges and the measurement of the blur width in pixels around an obviously sharp object. The obtained results are encouraging, which confirms the good performance of the proposed approach.

scholarly journals Dark Channel Constraint and Alternated Direction Multiplier Optimization of Turbulence Degraded Image Blind Restoration

2018 ◽  
Vol 36 (1) ◽  
pp. 103-109
Author(s):  
Huihui Li ◽  
Lun Yu ◽  
Liang Zhang ◽  
Ning Yang
Keyword(s):  
Image Restoration ◽  
Prior Information ◽  
Descent Method ◽  
Variational Model ◽  
Posteriori Probability ◽  
A Posteriori Probability ◽  
Dark Channel ◽  
Degraded Image ◽  
Blind Restoration ◽  
Total Variational Model

In order to improve the effect of turbulence degraded image restoration, aiming at the problem that the fuzzy solution is easy to be obtained by using the prior information constraint of gradient distribution under the framework of maximum a posteriori probability of blind restoration algorithm, this paper proposes a dark channel constraint and alternated direction multiplier optimization of turbulence degraded image blind restoration method.First, based on the idea of multi-scale, a dark channel prior constraint is imposed on the image and non-negative constraints and energy constraints are imposed on the point spread function at each level.Then, the kernel and image of the current scale are estimated by alternating iterations of coordinate descent method. When the maximum scale is reached, the final estimated blur kernel is obtained.Last, combined with the total variational model, the image details are quickly restored using the alternate direction optimization method. The experimental results show that the prior information constraint used in the proposed algorithm is advantageous to obtain a clear solution, and can converge to the global optimal solution in the total variational model, which can effectively suppress the artifacts produced in the image restoration process and recover a better target image detail.

Turbulence-Degraded Image Blind Restoration Method Using Accelerated and Damped Richardson-Lucy Algorithm

2011 ◽  
Vol 48 (8) ◽  
pp. 081001 ◽  
Author(s):  
李勇 Li Yong ◽  
范承玉 Fan Chengyu ◽  
时东锋 Shi Dongfeng ◽  
王海涛 Wang Haitao ◽  
冯晓星 Feng Xiaoxing ◽  
...  
Keyword(s):  
Degraded Image ◽  
Blind Restoration ◽  
Restoration Method

scholarly journals BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK

2021 ◽  
Vol 1 (1) ◽  
pp. 25-32
Author(s):  
Meryem H. Muhson ◽  
Ayad A. Al-Ani
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Super Resolution ◽  
Degraded Image ◽  
Blind Restoration ◽  
High Resolution Images ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Blurred Images ◽  
Degraded Images

Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image. This research aims to restore blurred images that have been corrupted by a known or unknown degradation function. Image restoration approaches can be classified into 2 groups based on degradation feature knowledge: blind and non-blind techniques. In our research, we adopt the type of blind algorithm. A deep learning method (SR) has been proposed for single image super-resolution. This approach can directly learn an end-to-end mapping between low-resolution images and high-resolution images. The mapping is expressed by a deep convolutional neural network (CNN). The proposed restoration system must overcome and deal with the challenges that the degraded images have unknown kernel blur, to deblur degraded images as an estimation from original images with a minimum rate of error.  

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.

scholarly journals Blind Deblurring Based on Sigmoid Function

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3484
Author(s):  
Shuhan Sun ◽  
Lizhen Duan ◽  
Zhiyong Xu ◽  
Jianlin Zhang
Keyword(s):  
Performance Optimization ◽  
Degradation Process ◽  
Image Deconvolution ◽  
Sigmoid Function ◽  
Blind Image Deblurring ◽  
Linear Estimators ◽  
Blind Deblurring ◽  
Degraded Image ◽  
Excellent Property ◽  
Blind Image

Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm based on the sigmoid function, which constructs novel blind deblurring estimators for both the original image and the degradation process by exploring the excellent property of sigmoid function and considering image derivative constraints. Owing to these symmetric and non-linear estimators of low computation complexity, high-quality images can be obtained by the algorithm. The algorithm is also extended to image sequences. The sigmoid function enables the proposed algorithm to achieve state-of-the-art performance in various scenarios, including natural, text, face, and low-illumination images. Furthermore, the method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the algorithm can remove the blur effect and improve the image quality of actual and simulated images. Finally, the use of sigmoid function provides a new approach to algorithm performance optimization in the field of image restoration.

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