Research Of The Color Image Restoration Algorithm Based On Generative Adversarial Networks

With the rapid development of digital image technology and Internet technology,the application of image restoration technology is becoming more and more widespread.Image restoration problems have a very solid technical foundation in the field of image processing,and can provide important technical support for other related image processing tasks.The current stage of image restoration technology can be applied in the fields of security,art restoration and film,photography,etc.It has very important research significance and has become one of the key research directions in the field of computer vision.The main work of this paper is to consider the shortcomings of the current image restoration algorithms,such as the inability to correlate the information of multilevel receptive fields and the lack of attention distribution to the convolution module,and propose corresponding solutions.Combining with generative adversarial network algorithms,this paper designs an algorithm model for image repair tasks.A multi-level information fusion module is used to introduce information of different levels of receptive fields,and a more targeted attention mechanism and a multi-channel convolution module are added,which can effectively optimize the defects of the existing model.The specific work is as follows:First of all,this paper introduces the research background of image repair tasks and the current research status at home and abroad,analyzes the research trends of existing algorithms,briefly introduces traditional image repair algorithms and image learning algorithms based on deep learning.A systematic brief description of the work to be performed.Secondly,this paper analyzes the application requirements of image repair tasks,and the existing models are still difficult to meet.In view of the shortcomings of image repair algorithms based on deep learning,improved ideas are proposed.The algorithm theory on which this article relies has been introduced,and the relevant algorithm principles have been analyzed from multiple angles,which provides a basis for the subsequent work of this article.Subsequently,the image restoration model designed in this paper is described,and our work is introduced in many aspects from the network structure,attention module,multi-channel convolution model,loss function design,etc.,compared with the baseline,and analyzed in this paper.Improvements made.Finally,in order to verify the effectiveness of the proposed method,a comparative experiment was performed on the Image Net image dataset.The model in this paper achieved 29.33 and 0.907 on the indicators of structural similarity and peak signal-to-noise ratio,respectively.The repair results are more natural at the context connection,and the texture structure has higher credibility.The comparison results with the benchmark network and leading-edge image restoration algorithms show that the algorithm in this paper has significantly better performance in both quantitative evaluation indicators(ie,structural similarity and peak signal-to-noise ratio)and subjective restoration result perception.It is proved that the algorithm in this paper can effectively improve the performance of existing models.