A modified image restoration algorithm for multiframe degraded images

2009 ◽  
Author(s):  
Zexun Geng ◽  
Zhenlei Zhao ◽  
Xiang Song
Keyword(s):  
Image Restoration ◽  
Restoration Algorithm ◽  
Degraded Images

A Novel Image Contrast Restoration Algorithm for Fog

2013 ◽  
Vol 709 ◽  
pp. 534-537
Author(s):  
Hui Xian Lv ◽  
Zhi Gang Zhao ◽  
Yan Feng Xu
Keyword(s):  
Image Restoration ◽  
Histogram Equalization ◽  
Low Contrast ◽  
Filtering Method ◽  
Restoration Algorithm ◽  
Homomorphic Filtering ◽  
Degraded Image ◽  
Soft Threshold ◽  
Degraded Images ◽  
Novel Algorithm

Images captured in fog suffer from low contrast, restoration of fog- degraded images are needed. In this paper, a novel algorithm of image restoration based on wavelet semi-soft threshold is presented. The results show detail restoration and de-noising are improved effectively comparing with Histogram equalization and homomorphic filtering method. It can be concluded that the new algorithm enhanced the contrast of fog-degraded image well.

ARTIFICIAL NEURAL NETWORKS FOR BLUR IDENTIFICATION AND RESTORATION OF NONLINEARLY DEGRADED IMAGES

2001 ◽  
Vol 11 (05) ◽  
pp. 455-461 ◽  
Author(s):  
T. A. CHEEMA ◽  
I. M. QURESHI ◽  
A. JALIL ◽  
A. NAVEED
Keyword(s):  
Image Restoration ◽  
Error Function ◽  
Nonlinear Phenomena ◽  
Model Combining ◽  
Restoration Algorithm ◽  
Blur Identification ◽  
Blurred Image ◽  
Gradient Based ◽  
Blurred Images ◽  
Degraded Images

In this paper, an image restoration algorithm is proposed to identify noncausal blur function. Image degradation processes include both linear and nonlinear phenomena. A neural network model combining an adaptive auto-associative network with a random Gaussian process is proposed to restore the blurred image and blur function simultaneously. The noisy and blurred images are modeled as continuous associative networks, whereas auto-associative part determines the image model coefficients and the hetero-associative part determines the blur function of the system. The self-organization like structure provides the potential solution of the blind image restoration problem. The estimation and restoration are implemented by using an iterative gradient based algorithm to minimize the error function.

Image Restoration Based on Improved PSO Algorithm

2012 ◽  
Vol 605-607 ◽  
pp. 2249-2252
Author(s):  
Zhi Yu Ye
Keyword(s):  
Particle Swarm Optimization ◽  
Image Restoration ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Image Noise ◽  
Swarm Optimization ◽  
Restoration Algorithm ◽  
Different Types ◽  
Local Optimal Solution ◽  
Degraded Images

An image restoration algorithm based on improved particle swarm optimization is presented. The algorithm has the ability of searching fast, and can effectively avoid the local optimal solution. Experimental results indicate that this algorithm is insensitive to image noise, and can be effective to different types of degraded images restoration.

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.

Image Restoration Algorithm for Single Nighttime Weakly Illuminated Haze Image

2018 ◽  
Vol 30 (3) ◽  
pp. 459
Author(s):  
Chunming Tang ◽  
Yancheng Dong ◽  
Xin Sun ◽  
Jun Lin ◽  
Zheng Lian
Keyword(s):  
Image Restoration ◽  
Restoration Algorithm

scholarly journals Compressed sensing image restoration algorithm based on improved SURF operator

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 1033-1045
Author(s):  
Guodong Zhou ◽  
Huailiang Zhang ◽  
Raquel Martínez Lucas
Keyword(s):  
Compressed Sensing ◽  
Image Restoration ◽  
Feature Vector ◽  
Optimization Method ◽  
Image Feature ◽  
Diffusion Method ◽  
Local Similarity ◽  
Weighting Method ◽  
Data Smoothing ◽  
Restoration Algorithm

Abstract Aiming at the excellent descriptive ability of SURF operator for local features of images, except for the shortcoming of global feature description ability, a compressed sensing image restoration algorithm based on improved SURF operator is proposed. The SURF feature vector set of the image is extracted, and the vector set data is reduced into a single high-dimensional feature vector by using a histogram algorithm, and then the image HSV color histogram is extracted.MSA image decomposition algorithm is used to obtain sparse representation of image feature vectors. Total variation curvature diffusion method and Bayesian weighting method perform image restoration for data smoothing feature and local similarity feature of texture part respectively. A compressed sensing image restoration model is obtained by using Schatten-p norm, and image color supplement is performed on the model. The compressed sensing image is iteratively solved by alternating optimization method, and the compressed sensing image is restored. The experimental results show that the proposed algorithm has good restoration performance, and the restored image has finer edge and texture structure and better visual effect.

Sign in / Sign up

Export Citation Format

Share Document