Lightweight Underwater Image Enhancement via Impulse Response of Low-Pass Filter Based Attention Network

In this paper, we propose an improved model of ShallowUWnet 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 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.