Blurred Image Blind Super-resolution Network via Kernel Estimation

被引：0
|
作者：
Li G.-P. [1 ]
Lu Y. [1 ]
Wang Z.-J. [1 ]
Wu Z.-W. [1 ]
Wang S.-Z. [1 ]
机构：
[1] School of Computer Science and Technology, Beijing Institute of Technology, Beijing
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2023年 / 49卷 / 10期
基金：
中国国家自然科学基金;
关键词：
blind super-resolution; blur kernel estimation; Blurred image; convolutional neural network;
D O I：
10.16383/j.aas.c200987
中图分类号：
学科分类号：
摘要：
Blind blurred image super-resolution is challenging and has important application values. This paper proposes a blurred image blind super-resolution network via kernel estimation (BESRNet), which mainly includes two parts: Blur kernel estimation network (BKENet) and kernel adaptive super-resolution network (SRNet). Given a low-resolution image (LR), the network uses the blur kernel estimation subnetwork to estimate the blur kernel from the input image, and then it uses the kernel adaptive super-resolution subnetwork to super-resolve the input low-resolution image. Different from other blind super-resolution methods, the proposed blur kernel estimation subnetwork gives the whole blur kernel, then the kernel adaptive super-resolution subnetwork dynamically adjusts the image features of different network layers according to the estimated blur kernel to adapt to different image degradations. In this paper, extensive experiments are carried out on multiple benchmark datasets. The qualitative and quantitative results show that proposed method is superior to other blind super-resolution methods. © 2023 Science Press. All rights reserved.
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收藏
页码：2109 / 2121
页数：12
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