scholarly journals Binarization of Degraded Document Images Using Convolutional Neural Networks and Wavelet-Based Multichannel Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 153517-153534
Author(s):  
Younes Akbari ◽  
Somaya Al-Maadeed ◽  
Kalthoum Adam
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Document Images ◽  
Multichannel Images ◽  
Degraded Document

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

Simple and Efficient Document Image Binarization Technique For Degraded Document Images

2012 ◽  
Vol 3 (5) ◽  
pp. 217-220
Author(s):  
Manju Joseph ◽  
◽  
Jijina K.P Jijina K.P
Keyword(s):  
Document Image ◽  
Document Images ◽  
Image Binarization ◽  
Document Image Binarization ◽  
Degraded Document

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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