scholarly journals A Nonlinear Image Restoration Framework Using Vector Quantization

2005 ◽  
Author(s):  
E.Y. Lam
Keyword(s):  
Image Restoration ◽  
Vector Quantization ◽  
Nonlinear Image Restoration

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

Effective Multiple Vector Quantization for Image Compression

2007 ◽  
Vol 11 (10) ◽  
pp. 1189-1196
Author(s):  
Noritaka Shigei ◽  
◽  
Hiromi Miyajima ◽  
Michiharu Maeda ◽  
Lixin Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Image Restoration ◽  
Vector Quantization ◽  
High Quality ◽  
Index Coding ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Compressed Data

Multiple-VQ methods generate multiple independent codebooks to compress an image by using a neural network algorithm. In the image restoration, the methods restore low quality images from the multiple codebooks, and then combine the low quality ones into a high quality one. However, the naive implementation of these methods increases the compressed data size too much. This paper proposes two improving techniques to this problem: “index inference” and “ranking based index coding.” It is shown that index inference and ranking based index coding are effective for smaller and larger codebook sizes, respectively.

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