Research on Motion Blur Image Restoration Algorithm Based on Improved Wiener Filter

2020 ◽  
Author(s):  
Guanyang Zhang ◽  
Songqing You ◽  
Kaikai Wu
Keyword(s):  
Image Restoration ◽  
Wiener Filter ◽  
Motion Blur ◽  
Restoration Algorithm

scholarly journals New Cepstrum Based Image Restoration Algorithm for Grayscale Images

2019 ◽  
Vol 8 (11) ◽  
pp. 1920-1928
Keyword(s):  
Image Restoration ◽  
Heuristic Algorithms ◽  
Bat Algorithm ◽  
Motion Blur ◽  
Hybrid Technique ◽  
Restoration Algorithm ◽  
Blur Estimation ◽  
Blurred Images ◽  
Selection Of ◽  
Grayscale Images

Optimization is the process that relates to finding the most excellent ways for all possible solutions. From last 2-3 decades, natural algorithms play an important role in improving solutions of various problems. By comparing various meta-heuristic algorithms, researchers can make a choice to the best selection of the meta-heuristic algorithms for the proposed problem. In this particular research, we have applied New Cepstrum based technique of image restoration to find out PSF parameters of motion blurred images as a primary technique. In addition, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), BAT Algorithm and GA-BAT hybrid technique etc. are also applied to optimize the blur parameters for calculated by new cepstrum based technique for blur estimation. This aids in analyzing the performance of each algorithm on the same primary technique. The performance analysis of all four algorithms aid in making the decision on the best meta-heuristic algorithm of the cepstrum based technique and to identify the preciseness of the motion blur. All four methods are applied to the same set of images. The algorithm is tested and compared using grayscale images and the benchmarking freely available online datasets, respectivel

scholarly journals Learning to Deblur Face Images via Sketch Synthesis

2020 ◽  
Vol 34 (07) ◽  
pp. 11523-11530 ◽  
Author(s):  
Songnan Lin ◽  
Jiawei Zhang ◽  
Jinshan Pan ◽  
Yicun Liu ◽  
Yongtian Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Domain Knowledge ◽  
Motion Blur ◽  
Deep Convolutional Neural Networks ◽  
Face Images ◽  
Restoration Algorithm ◽  
Deep Cnn ◽  
Blur Estimation ◽  
Face Deblurring

The success of existing face deblurring methods based on deep neural networks is mainly due to the large model capacity. Few algorithms have been specially designed according to the domain knowledge of face images and the physical properties of the deblurring process. In this paper, we propose an effective face deblurring algorithm based on deep convolutional neural networks (CNNs). Motivated by the conventional deblurring process which usually involves the motion blur estimation and the latent clear image restoration, the proposed algorithm first estimates motion blur by a deep CNN and then restores latent clear images with the estimated motion blur. However, estimating motion blur from blurry face images is difficult as the textures of the blurry face images are scarce. As most face images share some common global structures which can be modeled well by sketch information, we propose to learn face sketches by a deep CNN so that the sketches can help the motion blur estimation. With the estimated motion blur, we then develop an effective latent image restoration algorithm based on a deep CNN. Although involving the several components, the proposed algorithm is trained in an end-to-end fashion. We analyze the effectiveness of each component on face image deblurring and show that the proposed algorithm is able to deblur face images with favorable performance against state-of-the-art methods.

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.

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