Guided filter-based multi-scale super-resolution reconstruction
[摘要] The learning-based super-resolution reconstruction method inputs a low-resolution image into a network, and learns a non-linear mapping relationship between low-resolution and high-resolution through the network. In this study, the multi-scale super-resolution reconstruction network is used to fuse the effective features of different scale images, and the non-linear mapping between low resolution and high resolution is studied from coarse to fine to realise the end-to-end super-resolution reconstruction task. The loss of some features of the low-resolution image will negatively affect the quality of the reconstructed image. To solve the problem of incomplete image features in low-resolution, this study adopts the multi-scale super-resolution reconstruction method based on guided image filtering. The high-resolution image reconstructed by the multi-scale super-resolution network and the real high-resolution image are merged by the guide image filter to generate a new image, and the newly generated image is used for secondary training of the multi-scale super-resolution reconstruction network. The newly generated image effectively compensates for the details and texture information lost in the low-resolution image, thereby improving the effect of the super-resolution reconstructed image.Compared with the existing super-resolution reconstruction scheme, the accuracy and speed of super-resolution reconstruction are improved.
[发布日期] [发布机构]
[效力级别] [学科分类] 数学（综合）
[关键词] image resolution;image texture;learning (artificial intelligence);image reconstruction;image filtering;high-resolution image;low-resolution image loss;super-resolution reconstruction effect;guided filter-based multiscale super-resolution reconstruction;learning-based super-resolution reconstruction method;multiscale super-resolution reconstruction network;end-to-end super-resolution reconstruction task;multiscale super-resolution reconstruction method;guided image filtering;multiscale super-resolution network;guide image filter;newly generated image;super-resolution reconstruction scheme;B0290F Interpolation and function approximation (numerical analysis);B6135 Optical;image and video signal processing;B6140B Filtering methods in signal processing;C5260B Computer vision and image processing techniques;C6170K Knowledge engineering techniques [时效性]
