Noise removal for degraded images by IBS shrink method in multiwavelet domain

2007 ◽  
Vol 90 (7) ◽  
pp. 15-24 ◽  
Author(s):  
Jianming Lu ◽  
Ling Wang ◽  
Yeqiu Li ◽  
Takashi Yahagi
Keyword(s):  
Noise Removal ◽  
Degraded Images

LEARNING ITERATIVE IMAGE RECONSTRUCTION IN THE NEURAL ABSTRACTION PYRAMID

2001 ◽  
Vol 01 (04) ◽  
pp. 427-438 ◽  
Author(s):  
SVEN BEHNKE
Keyword(s):  
Neural Networks ◽  
Image Reconstruction ◽  
Recurrent Neural Networks ◽  
Multiple Scales ◽  
Noise Removal ◽  
Hierarchical Architecture ◽  
Context Information ◽  
Analysis And Synthesis ◽  
Local Connection ◽  
Degraded Images

Successful image reconstruction requires the recognition of a scene and the generation of a clean image of that scene. We propose to use recurrent neural networks for both analysis and synthesis. The networks have a hierarchical architecture that represents images in multiple scales with different degrees of abstraction. The mapping between these representations is mediated by a local connection structure. We supply the networks with degraded images and train them to reconstruct the originals iteratively. This iterative reconstruction makes it possible to use partial results as context information to resolve ambiguities. We demonstrate the power of the approach using three examples: superresolution, fill-in of occluded parts, and noise removal/contrast enhancement. We also reconstruct images from sequences of degraded images.

scholarly journals Noise Removal for Degraded Images with Impulse Noise Using Median Filters and Neural Filters

2003 ◽  
Vol 123 (6) ◽  
pp. 1072-1079 ◽  
Author(s):  
Noritaka Yamashita ◽  
Jianming Lu ◽  
Hiroo Sekiya ◽  
Takashi Yahagi
Keyword(s):  
Impulse Noise ◽  
Noise Removal ◽  
Median Filters ◽  
Degraded Images

scholarly journals Noise Removal for Degraded Images Using Neural Networks

2002 ◽  
Vol 122 (8) ◽  
pp. 1301-1308 ◽  
Author(s):  
Jianming LU ◽  
Minoru Fujimoto ◽  
Takashi YAHAGI
Keyword(s):  
Neural Networks ◽  
Noise Removal ◽  
Degraded Images

scholarly journals Noise Removal for Degraded Images Using Trimmed Filter and Neural Filters

2005 ◽  
Vol 125 (5) ◽  
pp. 774-782 ◽  
Author(s):  
Noritaka Yamashita ◽  
Jianming Lu ◽  
Hiroo Sekiya ◽  
Takashi Yahagi
Keyword(s):  
Noise Removal ◽  
Degraded Images

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.

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