GSDerainNet: A Deep Network Architecture Based on a Gaussian Shannon Filter for Single Image Deraining
-
Published:2023-10-05
Issue:19
Volume:15
Page:4825
-
ISSN:2072-4292
-
Container-title:Remote Sensing
-
language:en
-
Short-container-title:Remote Sensing
Author:
Yao Yanji123ORCID, Shi Zhimin12, Hu Huiwen12, Li Jing3, Wang Guocheng1, Liu Lintao1
Affiliation:
1. State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China 2. University of Chinese Academy of Sciences, Beijing 100049, China 3. School of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China
Abstract
With the continuous advancement of target detection technology in remote sensing, target detection technology in images captured by drones has performed well. However, object detection in drone imagery is still a challenge under rainy conditions. Rain is a common severe weather condition, and rain streaks often degrade the image quality of sensors. The main issue of rain streaks removal from a single image is to prevent over smoothing (or underclearing) phenomena. Aiming at the above problems, this paper proposes a deep learning (DL)-based rain streaks removal framework called GSDerainNet, which properly formulates the single image rain streaks removal problem; rain streaks removal is aptly described as a Gaussian Shannon (GS) filter-based image decomposition problem. The GS filter is a novel filter proposed by us, which consists of a parameterized Gaussian function and a scaled Shannon function. Two-dimensional GS filters exhibit high stability and effectiveness in dividing an image into low- and high-frequency parts. In our framework, an input image is first decomposed into a low-frequency part and a high-frequency part by using the GS filter. Rain streaks are located in the high-frequency part. We extract and separate the rain features of the high-frequency part through a deep convolutional neural network (CNN). The experimental results obtained on synthetic data and real data show that the proposed method can better suppress the morphological artifacts caused by filtering. Compared with state-of-the-art single image rain streaks removal methods, the proposed method retains finer image object structures while removing rain streaks.
Funder
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference45 articles.
1. Xi, Y., Jia, W., Miao, Q., Feng, J., Liu, X., and Li, F. (2023). CoDerainNet: Collaborative Deraining Network for Drone-View Object Detection in Rainy Weather Conditions. Remote Sens., 15. 2. SINet: A scale-insensitive convolutional neural network for fast vehicle detection;Hu;IEEE Trans. Intell. Transp. Syst.,2018 3. Computer vision for autonomous vehicles: Problems, datasets and state of the art;Janai;Foundations and Trends® in Computer Graphics and Vision,2020 4. Enhancing Rainy Weather Driving: Deep Unfolding Network with PGD Algorithm for Single Image Deraining;Hu;IEEE Access,2023 5. Li, R., Tan, R., Cheong, L., Aviles-Rivero, A., Fan, Q., and Schonlieb, C. (November, January 27). Rainflow: Optical flow under rain streaks and rain veiling effect. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea.
|
|