A novel image restoration model using ICA and ridgelet transform

2009 ◽  
Author(s):  
Guangming Zhang ◽  
Zhiming Cui
Keyword(s):  
Image Restoration ◽  
Ridgelet Transform ◽  
Restoration Model

Construction and solution of an adaptive image-restoration model for removing blur and mixed noise

2016 ◽  
Vol 25 (2) ◽  
pp. 023013
Author(s):  
Youquan Wang ◽  
Lihong Cui ◽  
Yigang Cen ◽  
Jianjun Sun
Keyword(s):  
Image Restoration ◽  
Mixed Noise ◽  
Restoration Model

An Adaptive Variational PDE Based Image Restoration Model Using Hopfield Neural Network

2011 ◽  
Vol 48-49 ◽  
pp. 174-178
Author(s):  
Wei Sun ◽  
Sheng Nan Liu
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Adaptive Regularization ◽  
Proposed Model ◽  
Degraded Image ◽  
Restoration Problem ◽  
Restoration Model

An adaptive variational partial differential equation (PDE) based aproach for restoration of gray level images degraded by a known shift-invariant blur function and additive noise is presented. The restoration problem of a degraded image is solved by minimizing this model, and this minimizing problem is realized by using Hopfield neural network. In the proposed image restoration model, an adaptive regularization parameter is developed instead of the constant regularization parameter used in previous PDE model. The value of the adaptive regularization parameter changes according to different regions of the image to remove noises and preserve edge better. Several computer simulation results show that the image restoration results of the proposed model both look better and have better SNR (Signal to Noise Ratio) than the previous variational PDE based model.

scholarly journals A Partial Differential Equation-Based Image Restoration Method in Environmental Art Design

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chen Li
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Image Restoration ◽  
Rapid Development ◽  
Real Life ◽  
Generative Adversarial Networks ◽  
Environmental Art ◽  
Partial Differential ◽  
Art Design ◽  
Restoration Model

With the rapid development of networks and the emergence of various devices, images have become the main form of information transmission in real life. Image restoration, as an important branch of image processing, can be applied to real-life situations such as pixel loss in image transmission or network prone to packet loss. However, existing image restoration algorithms have disadvantages such as fuzzy restoration effect and slow speed; to solve such problems, this paper adopts a dual discriminator model based on generative adversarial networks, which effectively improves the restoration accuracy by adding local discriminators to track the information of local missing regions of images. However, the model is not optimistic in generating reasonable semantic information, and for this reason, a partial differential equation-based image restoration model is proposed. A classifier and a feature extraction network are added to the dual discriminator model to provide category, style, and content loss constraints to the generative network, respectively. To address the training instability problem of discriminator design, spectral normalization is introduced to the discriminator design. Extensive experiments are conducted on a data dataset of partial differential equations, and the results show that the partial differential equation-based image restoration model provides significant improvements in image restoration over previous methods and that image restoration techniques are exceptionally important in the application of environmental art design.

Research on Image Restoration Methods

2015 ◽  
Vol 742 ◽  
pp. 277-280
Author(s):  
Yu Bing Dong ◽  
Hua Xun Zhang ◽  
Ying Sun
Keyword(s):  
Image Restoration ◽  
Histogram Equalization ◽  
New Method ◽  
Wiener Filtering ◽  
Median Filtering ◽  
Inverse Filtering ◽  
Matlab Simulation ◽  
Paper Degradation ◽  
Restoration Method ◽  
Restoration Model

In the paper, degradation and restoration model is introduced. Image restoration method using inverse filtering and using wiener filtering are studied and implemented. A new method of image restoration is proposed by combining histogram equalization and median filtering. Comparing three methods by MATLAB simulation, the results show that the new method can effectively restore degradation image with comparatively high restoration efficiency.

A Blind Restoration Approach for Defocused Barcode Images

2017 ◽  
Vol 8 (3) ◽  
pp. 41-58
Author(s):  
Shamik Tiwari
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Radial Basis Function ◽  
Basis Function ◽  
Accurate Estimation ◽  
Robust Method ◽  
Ridgelet Transform ◽  
Accurate Representation ◽  
Blurred Image ◽  
Blind Restoration

Use of a mobile camera for barcode decoding provides high portability and availability but it requires that the recorded barcode image must be accurate representation of the barcode that is available on the product. Barcode scanning is challenging because images may be degraded due to out-of-focus blur at the time of image acquisition. Therefore, image restoration is essential in making image sharp and useful. In case of blind restoration of such barcode images accurate estimation of out-of-focus blur parameter is highly desirable. In this article, a robust method has been proposed for estimating the radius of out-of-focus blur. Finite discrete ridgelet transform has been used to find the features of the blurred image and a radial basis function neural network is utilized to estimate the radius of out-of-focus blur. The experimental results reveal that proposed method more robust than the existing methods.

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