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

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.

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Research and Application of Ancient Chinese Pattern Restoration Based on Deep Convolutional Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/2691346 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Xuhui Fu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Restoration ◽

Deep Convolutional Neural Network ◽

Deep Convolutional Neural Networks ◽

Multilayer Network ◽

Restoration Method ◽

Restoration Effect

In recent years, deep learning, as a very popular artificial intelligence method, can be said to be a small area in the field of image recognition. It is a type of machine learning, actually derived from artificial neural networks, and is a method used to learn the characteristics of sample data. It is a multilayer network, which can learn the information from the bottom to the top of the image through the multilayer network, so as to extract the characteristics of the sample, and then perform identification and classification. The purpose of deep learning is to make the machine have the same analytical and learning capabilities as the human brain. The ability of deep learning in data processing (including images) is unmatched by other methods, and its achievements in recent years have left other methods behind. This article comprehensively reviews the application research progress of deep convolutional neural networks in ancient Chinese pattern restoration and mainly focuses on the research based on deep convolutional neural networks. The main tasks are as follows: (1) a detailed and comprehensive introduction to the basic knowledge of deep convolutional neural and a summary of related algorithms along the three directions of text preprocessing, learning, and neural networks are provided. This article focuses on the related mechanism of traditional pattern repair based on deep convolutional neural network and analyzes the key structure and principle. (2) Research on image restoration models based on deep convolutional networks and adversarial neural networks is carried out. The model is mainly composed of four parts, namely, information masking, feature extraction, generating network, and discriminant network. The main functions of each part are independent and interdependent. (3) The method based on the deep convolutional neural network and the other two methods are tested on the same part of the Qinghai traditional embroidery image data set. From the final evaluation index of the experiment, the method in this paper has better evaluation index than the traditional image restoration method based on samples and the image restoration method based on deep learning. In addition, from the actual image restoration effect, the method in this paper has a better image restoration effect than the other two methods, and the restoration results produced are more in line with the habit of human observation with the naked eye.

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

Sensors ◽

10.3390/s90604649 ◽

2009 ◽

Vol 9 (6) ◽

pp. 4649-4668 ◽

Cited By ~ 4

Author(s):

Amar A. El-Sallam ◽

Farid Boussaid

Keyword(s):

High Resolution ◽

Image Restoration ◽

Color Image ◽

Imaging Systems ◽

Restoration Algorithm

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A new color image restoration algorithm based on LAB and RBF neural network

2012 IEEE International Conference on Mechatronics and Automation ◽

10.1109/icma.2012.6285693 ◽

2012 ◽

Author(s):

Liwei Zhang ◽

Yaping Zhang

Keyword(s):

Neural Network ◽

Image Restoration ◽

Color Image ◽

Rbf Neural Network ◽

Restoration Algorithm

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An Objective Evaluation Method for Image Restoration

Journal of Electrical and Computer Engineering ◽

10.1155/2010/172081 ◽

2010 ◽

Vol 2010 ◽

pp. 1-6

Author(s):

A. Lakshmi ◽

Subrata Rakshit

Keyword(s):

Computer Vision ◽

Image Restoration ◽

Evaluation Method ◽

Objective Evaluation ◽

Quantitative Comparison ◽

Experimental Results ◽

Original Image ◽

Restoration Method ◽

Restoration Algorithms ◽

Systems Effectiveness

Accurate image restoration is of paramount importance for low-level vision, computer vision, and various other fields. Numerous complex restoration algorithms have been stated in the literature. Performance of these restoration algorithms differs with the nature of the image and distortion. These algorithms are evaluated either qualitatively or quantitatively by comparing the restored image with the original image. The practical drawback of this quantitative comparison is the requirement for the original image. In this paper, we propose a measure, with theoretical grounding, to objectively evaluate the restoration algorithms with no knowledge about the original image. This measure analyses the deblurring as well as the denoising nature of the restoration method. The main utility of this measure will be in designing automatic restoration systems. Effectiveness of this measure is substantiated with experimental results.

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