Iterative algorithms with fast-convergence rates in nonlinear image restoration

1991 ◽  
Author(s):  
Michael E. Zervakis ◽  
Anastasios N. Venetsanopoulos
Keyword(s):  
Image Restoration ◽  
Convergence Rates ◽  
Iterative Algorithms ◽  
Fast Convergence ◽  
Nonlinear Image Restoration

scholarly journals Deep Residual Network with Sparse Feedback for Image Restoration

2018 ◽  
Vol 8 (12) ◽  
pp. 2417 ◽  
Author(s):  
Zhenyu Guo ◽  
Yujuan Sun ◽  
Muwei Jian ◽  
Xiaofeng Zhang
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Deep Neural Network ◽  
Convergence Speed ◽  
Fast Convergence ◽  
Experimental Results ◽  
Residual Network ◽  
Unknown Parameters ◽  
Noisy Images ◽  
Blurred Images

A deep neural network is difficult to train due to a large number of unknown parameters. To increase trainable performance, we present a moderate depth residual network for the restoration of motion blurring and noisy images. The proposed network has only 10 layers, and the sparse feedbacks are added in the middle and the last layers, which are called FbResNet. FbResNet has fast convergence speed and effective denoising performance. In addition, it can also reduce the artificial Mosaic trace at the seam of patches, and visually pleasant output results can be produced from the blurred images or noisy images. Experimental results show the effectiveness of our designed model and method.

Analytical tools for nonlinear image restoration

1983 ◽  
Vol 22 (10) ◽  
pp. 1424 ◽  
Author(s):  
William A. Porter ◽  
Philip D. Olivier
Keyword(s):  
Image Restoration ◽  
Nonlinear Image Restoration ◽  
Analytical Tools

M-estimators in robust nonlinear image restoration

1990 ◽  
Vol 29 (5) ◽  
pp. 455 ◽  
Author(s):  
Anastasios N. Venetsanopoulos
Keyword(s):  
Image Restoration ◽  
Nonlinear Image Restoration
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