A single-frame image super-resolution method based on video coding

Abstract

The invention discloses a single-frame image super-resolution method based on video coding, which uses the prior information that can be directly obtained in video coding to perform targeted processing on different sub-blocks of the image, and uses a complex network to process texture updates. For complex sub-blocks, an adaptive convolution module is designed to process sub-blocks of different coding modes, making the network more targeted, recovering different detail information for different textures, thereby improving the accuracy of super-resolution results . The present invention shares the parameters of the network with few channels into the network with deep channels, that is, achieves the super-resolution process of an entire picture with different layers of a backbone network, and uses relatively simple, shallow, and network processing with few channels Relatively large, smoother-textured sub-blocks reduce the time required for the super-resolution process.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a single-frame image super-resolution method based on video coding. Background technique [0002] Image super-resolution is the process of converting an input low-resolution visual image into a high-resolution visual image. An important focus of recent super-resolution work is to propose a variety of networks that accelerate the inference process. One of the branches is to achieve efficient super-resolution work with fewer parameters and faster speed. For example, the early FSRCNN directly extracts features from the input image, and then the feature map passes through an upsampling network to complete the construction of the super-resolution image. Another example is the recent work CARN, which uses group convolution technology to design a residual network to achieve fast processing of input images. Another branch is to increase the complexity of the network model, in...

AI Technical Summary

Problems solved by technology

[0004] In the traditional super-resolution network, the feature map is directly extracted from the whole picture. This structure makes the network unable to learn the different features of each region well, and the same convolution kernel is applied to process different regions to restore The texture details of the output image do not match the real image

And because the complexity of texture details in different areas of the image is different, the complex processing of low-detail areas often increases the computational load of the network unnecessarily.

However, as proposed by ClassSR, the neural network that classifies first and then passes through three parameters that are not shared will make it take a lot of time and computing power during training, increasing the complexity of the network.

In addition to the shortcomings mentioned above, most of today's super-resolution methods ignore the help of the prior information of the original image for the image super-resolution process.

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