Abstract
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.
Highlights
With the rapid development of science and technology, especially the development of computer technology, people began to associate images with computers [1, 2]
Images are an important way for us to obtain information. ere is a lot of information in life that cannot be clearly expressed by language alone, but through an image, it can be clear at a glance, showing the importance of the image
In order to reproduce the complete information of the image or remove some unwanted objects, digital image restoration technology has emerged at the right time and has been continuously developed and matured, and it has been reflected in this period
Summary
With the rapid development of science and technology, especially the development of computer technology, people began to associate images with computers [1, 2]. E dictionary structure constructed by this method cannot be updated and changed, so it cannot adapt to various image restoration situations Another method is to learn from training samples to obtain a sparse dictionary [24, 25]. E digital image restoration technology automatically detects and repairs damaged areas by inputting the corresponding algorithm into the computer, so this technology is currently the most direct and effective method for restoring works of art and old photos. By analyzing the structural information of the repair block in different regions such as texture, edge, and smoothing, we adaptively determine the size of the dictionary atom.
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