Sequential and parallel image restoration: neural network implementations

1994 ◽  
Vol 3 (6) ◽  
pp. 789-801 ◽  
Author(s):  
M.A.T. Figueiredo ◽  
J.M.N. Leitao
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Parallel Image

scholarly journals Image Restoration by Learning Morphological Opening-Closing Network

2020 ◽  
Vol 4 (1) ◽  
pp. 87-107
Author(s):  
Ranjan Mondal ◽  
Moni Shankar Dey ◽  
Bhabatosh Chanda
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
State Of The Art ◽  
Source Code ◽  
Back Propagation ◽  
Image Features ◽  
Main Difficulty ◽  
The Right ◽  
Right Order ◽  
Morphological Opening

AbstractMathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net

Hopfield neural network-based image restoration with adaptive mixed-norm regularization

2009 ◽  
Vol 7 (8) ◽  
pp. 686-689 ◽  
Author(s):  
许元男 Yuannan Xu ◽  
刘丽萍 Liping Liu ◽  
赵远 Yuan Zhao ◽  
靳辰飞 Chenfei Jin ◽  
孙秀冬 Xiudong Sun
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Hopfield Neural Network ◽  
Mixed Norm

HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network

2019 ◽  
Vol 57 (2) ◽  
pp. 667-682 ◽  
Author(s):  
Yi Chang ◽  
Luxin Yan ◽  
Houzhang Fang ◽  
Sheng Zhong ◽  
Wenshan Liao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Restoration ◽  
Hyperspectral Image

scholarly journals Denoising Prior Driven Deep Neural Network for Image Restoration

2019 ◽  
Vol 41 (10) ◽  
pp. 2305-2318 ◽  
Author(s):  
Weisheng Dong ◽  
Peiyao Wang ◽  
Wotao Yin ◽  
Guangming Shi ◽  
Fangfang Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Deep Neural Network

scholarly journals Denosing CT/MRI Images Restoration using Radial Basis Function Neural Network

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1364-1369
Keyword(s):  
Neural Network ◽  
Medical Imaging ◽  
Image Restoration ◽  
Radial Basis Function ◽  
Image Denoising ◽  
Basis Function ◽  
Medical Examination ◽  
Imaging Technology ◽  
Large Numbers ◽  
Original Domain

Medical imaging technology is becoming an important component of large numbers of applications such as diagnosis, treatment, survey and medical examination. Image restoration manages conveying back the bended image to its original domain. It re-establishes the corrupted image into keener image. This paper centers around evacuation of noise strategies in medical images with denoising a point by point overview has been completed on various image denoising methods and their exhibitions were evaluated and it is an activity to examine and evaluate various variations of denoising methods to enhance their execution and visual standard.

scholarly journals Contribution to image restoration using a neural network model

2002 ◽  
Vol Volume 1, 2002 ◽  
Author(s):  
Karim Achour ◽  
Nadia Zenati ◽  
Oualid Djekoune
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Network Model ◽  
Neural Network Model ◽  
Hopfield Neural Network ◽  
Optimization Method ◽  
Nous Proposons ◽  
International Audience ◽  
High Level

International audience The reduction of the blur and the noise is an important task in image processing. Indeed, these two types of degradation are some undesirable components during some high level treatments. In this paper, we propose an optimization method based on neural network model for the regularized image restoration. We used in this application a modified Hopfield neural network. We propose two algorithms using the modified Hopfield neural network with two updating modes : the algorithm with a sequential updates and the algorithm with the n-simultaneous updates. The quality of the obtained result attests the efficiency of the proposed method when applied on several images degraded with blur and noise. La réduction du bruit et du flou est une tâche très importante en traitement d'images. En effet, ces deux types de dégradations sont des composantes indésirables lors des traitements de haut niveau. Dans cet article, nous proposons une méthode d'optimisation basée sur les réseaux de neurones pour résoudre le problème de restauration d'images floues-bruitées. Le réseau de neurones utilisé est le réseau de « Hopfield ». Nous proposons deux algorithmes utilisant deux modes de mise à jour: Un algorithme avec un mode de mise à jour séquentiel et un algorithme avec un mode de mise à jour n-simultanée. L'efficacité de la méthode mise en œuvre a été testée sur divers types d'images dégradées.

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