scholarly journals Analysis and Comparative Study of Image Restoration by using Matlab

2018 ◽  
Vol Volume-2 (Issue-4) ◽  
pp. 1259-1263
Author(s):  
Vaishali Kumari ◽  
Ranjan Kumar Singh ◽  
2020 ◽  
Vol 17 (9) ◽  
pp. 4571-4579
Author(s):  
Rajbir Singh ◽  
Sumit Bansal

The method of recovering a true image from degraded one, to analyze that digital image and characteristics with no artifact errors is known as Image Restoration. These techniques are of two types: direct methods and indirect methods. Direct methods are those in which the results of image restoration are produced in one single step. Indirect methods are those in which the results of image restoration are produced after various steps. This method is termed as blind image deconvolution, when the known info is just the blurred digital image and no info about the (Point Spread Function) (PSF) or the degrading model. The target of the procedure is to recover both the latent (un-blurred) image and the blur kernel, simultaneously. In this paper, we presented a comprehensive research of image noise model,de-blurring methods, blur types, and a comparative study of various deblurring methods. We have implemented number experiments to study these methods according to their performance, (Peak Signal to Noise Ratio) PSNR, (structural similarity) SSIM, blur type, and (Minimum Mean Square Error) MMSE.


2017 ◽  
Vol 2 (7) ◽  
pp. 23
Author(s):  
Amrutha Kulkarni ◽  
Shanta Rangaswamy ◽  
Manonmani S

Image restoration is a process of reconstruction or recovery of an image that has been corrupted or degraded by any degradation phenomenon. Image restoration techniques are inclined towards modeling the degradation and applying the inverse process in order to recover the original image. The critical goal of restoration techniques is to improve the quality of an image in some predefined manner. This present paper is a comparative study of image enhancement techniques used for improving the quality of a given image and evaluate it against the quality of a given image and evaluate it against SNR, PSNR, MSE, and SSIM as metrics.


2010 ◽  
Vol 17 (3) ◽  
pp. 123-129 ◽  
Author(s):  
Fan Fan ◽  
Kecheng Yang ◽  
Min Xia ◽  
Wei Li ◽  
Bo Fu ◽  
...  

Author(s):  
J. I. de la Rosa ◽  
J. Villa-Hernandez ◽  
E. Gonzalez-Ramirez ◽  
E. de la Rosa M. ◽  
O. Gutierrez ◽  
...  

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