scholarly journals A High Resolution Color Image Restoration Algorithm for Thin TOMBO Imaging Systems

Sensors ◽  
2009 ◽  
Vol 9 (6) ◽  
pp. 4649-4668 ◽  
Author(s):  
Amar A. El-Sallam ◽  
Farid Boussaid
Keyword(s):  
High Resolution ◽  
Image Restoration ◽  
Color Image ◽  
Imaging Systems ◽  
Restoration Algorithm

scholarly journals Application of Adaptive Image Restoration Algorithm Based on Sparsity of Block Structure in Environmental Art Design

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Bo Liang ◽  
Xin-xin Jia ◽  
Yuan Lu
Keyword(s):  
Image Restoration ◽  
Color Image ◽  
Environmental Art ◽  
Special Effects ◽  
Structure Information ◽  
Film And Television ◽  
Restoration Algorithm ◽  
Restoration Method ◽  
Restoration Algorithms ◽  
Restoration Effect

Image restoration is a research hotspot in computer vision and computer graphics. It uses the effective information in the image to fill in the information of the designated damaged area. This has high application value in environmental design, film and television special effects production, old photo restoration, and removal of text or obstacles in images. In traditional sparse representation image restoration algorithms, the size of dictionary atoms is often fixed. When repairing the texture area, the dictionary atom will be too large to cause blurring. When repairing a smooth area, the dictionary atom is too small to cause the extension of the area, which affects the image repair effect. In this paper, the structural sparsity of the block to be repaired is used to adjust the repair priority. By analyzing the structure information of the repair block located in different regions such as texture, edge, and smoothing, the size of the dictionary atom is adaptively determined. This paper proposes a color image restoration method that adaptively determines the size of dictionary atoms and discusses a model based on the partial differential equation restoration method. Through simulation experiments combined with subjective and objective standards, the repair results are evaluated and analyzed. The simulation results show that the algorithm can effectively overcome the shortcomings of blurred details and region extension in fixed dictionary restoration, and the restoration effect has been significantly improved. Compared with the results of several other classic algorithms, it shows the effectiveness of the algorithm in this paper.

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 Full-color nanorouter for high-resolution imaging

Nanoscale ◽  
2021 ◽  
Author(s):  
Mingjie Chen ◽  
Long Wen ◽  
Dahui Pan ◽  
David Cumming ◽  
Xianguang Yang ◽  
...  
Keyword(s):  
High Resolution ◽  
Color Image ◽  
Image Sensors ◽  
Structural Color ◽  
High Resolution Imaging ◽  
Scaling Effects ◽  
Full Color ◽  
Resolution Imaging

Pixel scaling effects have been a major issue for the development of high-resolution color image sensors due to the reduced photoelectric signal and the color crosstalk. Various structural color techniques...

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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