Lightweight Underwater Image Enhancement Via Impulse Response of Low-pass Filter Based Attention Network
Year: 2024
Type: article
Abstract: In this paper, we propose an improved model of Shallow-UWnet for underwater image enhancement. In the proposed method, we enhance the learning process and solve the vanishing gradient problem by a skip connection, which concatenates the raw underwater image and the impulse response of the low-pass filter (LPF) into Shallow-UWnet. Additionally, we integrate the simple, parameter-free attention module (SimAM) into each Convolution Block to enhance the visual quality of images. Performance evaluations with state-of-the-art methods show that the proposed method has comparable results on EUVP-Dark, UFO-120, and UIEB datasets. Moreover, the proposed model has fewer trainable parameters and the resulting faster testing time is suitable for real-time processing in underwater image enhancement, which is particularly for resource-constrained underwater robots.
Source: IEEE SIGPORT
DOI: 10.60864/9hy0-e704
Authors: May Thet Tun, Yosuke Sugiura, Tetsuya Shimamura
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6977657070
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