Journal Article

Guided filter-based multi-scale super-resolution reconstruction

Li, Jinjiang, Hua, Zhen, Feng, Xiaomei

2020

Overview

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.

• Publisher: The Institution of Engineering and Technology,John Wiley & Sons, Inc,Wiley
• Subject: Algorithms / 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 / Deep learning / end-to-end super-resolution reconstruction task / guide image filter / guided filter-based multiscale super-resolution reconstruction / guided image filtering / high-resolution image / image filtering / Image filters / Image quality / Image reconstruction / Image resolution / image texture / learning (artificial intelligence) / learning-based super-resolution reconstruction method / low-resolution image loss / Mapping / Methods / multiscale super-resolution network / multiscale super-resolution reconstruction method / multiscale super-resolution reconstruction network / newly generated image / Research Article / Special effects / super-resolution reconstruction effect / super-resolution reconstruction scheme