Underwater Image Super-Resolution Using Frequency-Domain Enhanced Attention Network

Abstract

Underwater images super-resolution (SR) is a challenging task due to underwater images usually contain severely blurred details, color distortion, and low contrast. Although numerous deep learning-based methods have been developed to solve these problems, these methods suffer from huge model parameters and computations. To address this gap, we propose a frequency-domain enhanced attention network (FEAN), supported by a series of frequency-enhanced attention modules (FEAM), for accurate underwater SR. Specifically, we start by utilizing a Gaussian filter to decompose the features into high and low frequencies and pass them to the FEAM. Then, in the high-frequency path, we propose a multi-scale attention enhancement block (MAEB) to extract rich image texture information. While in the low-frequency path, we perform a simple convolutional operation to realize the brightness and contrast adjustment of the image. Further, we devise a channel attention fusion block (CAFB) to integrate the enhanced high and low-frequency features to further strengthen the powerful representational capability of the network. Finally, we employ two convolutions to further modulate the features on the high-frequency path for effective color bias correction and detail enhancement. Experimental results show that our FEAN performs better than other underwater SR methods on the USR-248 dataset, with PSNR values of 29.97 dB, 26.23 dB, and 23.99 dB, corresponding to $\times 2$ , $\times 4$ , and $\times 8$ scale factors.