JPEG image compression artifact elimination method based on controllable pyramid wavelet network

Abstract

The invention discloses a JPEG (Joint Photographic Experts Group) image compression artifact elimination method based on a controllable pyramid wavelet network, which comprises the following steps of: firstly, extracting an image feature related to a compression level, secondly, guiding to recover a Y channel image by using the feature, and then guiding to recover a CbCr channel image by using the feature and the recovered Y channel image, and finally, converting the image into an RGB space to obtain a final recovery result. According to the method provided by the invention, image coding parameter information does not need to be predicted, a good recovery effect can be achieved for a plurality of images with different compression levels, each recovery network only needs to train a single network model, on one hand, the model uses jump connection to avoid the problems of gradient disappearance and gradient explosion possibly occurring in the training process, and on the other hand, model complexity is reduced by using a recursion module sharing parameter strategy, and efficient operation of the algorithm is ensured. Therefore, the method provided by the invention has the advantages of simple model, few parameters, wide application range, remarkable recovery effect and the like.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a method for eliminating JPEG image compression artifacts based on a controllable pyramid wavelet network. Background technique [0002] Due to the limitation of transmission bandwidth and storage capacity, the image or video captured by the camera needs to be compressed during use. At present, the lossy compression method represented by JPEG compression has been widely used in various aspects of image processing. Since the high-frequency information of the image will be lost in the quantization stage, the compressed image will contain compression artifacts such as blocking artifacts, ringing, and blurring. The performance of various computer vision algorithms for input. Therefore, designing a fast and effective JPEG compressed image restoration algorithm has broad application prospects and practical value. [0003] At present, JPEG image compression artifact removal...

AI Technical Summary

Problems solved by technology

However, most of the current machine learning-based methods need to predict the coding information of compressed images, and are only effective for some compressed images, which limits the application range of the algorithm.

Although DnCNN overcomes the above limitations by adjusting the training data, its image restoration performance is general and only effective for grayscale images

In addition, there are also some methods to achieve multi-compression level image restoration tasks by training multiple network models, but multiple network models mean that more storage space is occupied

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