Guided filter-based multi-scale super-resolution reconstruction

• 作者: Jinjiang Li Xiaomei Feng Zhen Hua
• 来源期刊: 智能技术学报
• 学科: 工学
• 关键词: RESOLUTION IMAGE NETWORK
• 年，卷（期）: 2020,（2）
• 页码范围: 128-140
• 页数: 13页
• 分类号: TP3

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.