Unpaired sonar image denoising with simultaneous contrastive learning

Abstract

Implementing underwater sonar image denoising algorithms through unpaired clean and noisy images is feasible and meaningful, since compliant paired real underwater sonar data is nearly impractical. Nevertheless, acquiring high-performance denoising algorithm is challenging without pairs of data as learning objects. In addition, the wide domain gap between noisy and clean samples as well as the noise contained in underwater sonar images is significantly more complex than that involved in normal images. Therefore, directly utilizing current unpaired optimization algorithms (e.g. generative adversarial learning and circular consistency learning) in underwater sonar image denoising tasks is not sufficient to explore the essential connection from noisy inputs to clean outputs. In this paper, we propose a novel framework for unpaired sonar image denoising which probes the same features of unpaired samples through a dual contrast optimization approach in latent space, named as NCD-GAN. The designed algorithm is composed of two basic parts: Simultaneous Transition Structure (STS) and Separable Contrast Unit (SCU). Separately, STS adopts a cyclic structure of multi-domain transformation to generate valuable surface information, and mines potential feature distributions between different domains via the addition of bidirectional mappings. Meanwhile, SCU introduces additional constraints in layer domains to encourage closer background layers between adjacent domains, while increasing the distance between noise layers so as to better promote the removal of noise and assist in image restoration. Sufficient experiments confirm that our algorithm provides better performance than existing unpaired denoising algorithms on multiple publicly available sonar image datasets and exhibits excellent robustness in the processing of multiple noises. Compared to the latest denoising algorithms, there is at most a 26.43% improvement in the evaluation metrics. Furthermore, it gains a 22.75% mAP boost on the subsequent advanced visual tasks.