An improved blind restoration algorithm for multiframe turbulence-degraded images

2011 ◽  
Author(s):  
Jing Guan ◽  
Jianchong Chen ◽  
Kejia Yi ◽  
Ze Wang
Keyword(s):  
Restoration Algorithm ◽  
Blind Restoration ◽  
Degraded Images

Research on Realization of Medical Image Restoration Based on Blind Equalization Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 809-813
Author(s):  
Ting Han ◽  
Ruo-Han Zhao ◽  
Mo Dong
Keyword(s):  
Image Restoration ◽  
Medical Image ◽  
Medical Images ◽  
Blind Equalization ◽  
Diagnostic Errors ◽  
Degradation Process ◽  
Restoration Algorithm ◽  
Equalization Algorithm ◽  
Improvement Effect ◽  
Blind Restoration

In order to study the realization of medical image restoration, this study mainly adopts blind equalization algorithm to analyze medical images, and observes the improvement effect of blind equalization technology on medical images. In the process of medical image formation, it is unavoidable to be affected by point spread function, which leads to image degradation and brings great difficulties to diagnosis, and the results of degradation are often unpredictable. The results show that the blind restoration algorithm can restore the image when the degradation process of the medical image is uncertain, which makes the medical image clearer and more accurate, brings great convenience to the diagnosis, and also reduces the diagnostic errors caused by the unclear image.

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.

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