Guided filter-based multi-scale super-resolution reconstruction

Abstract

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.

Keywords

• 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