HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning

TL;DR

HUPE is a novel underwater image enhancement method that balances visual quality with practical application needs by using a reversible transformation, heuristic prior, and semantic collaborative learning to produce visually pleasing and task-oriented images.

Contribution

The paper introduces HUPE, a heuristic invertible network with semantic collaborative learning, for improved underwater perception enhancement that supports downstream tasks.

Findings

HUPE outperforms state-of-the-art methods in quantitative metrics.

HUPE produces visually pleasing images with better semantic features.

The method demonstrates versatility in downstream underwater perception tasks.

Abstract

Underwater images are often affected by light refraction and absorption, reducing visibility and interfering with subsequent applications. Existing underwater image enhancement methods primarily focus on improving visual quality while overlooking practical implications. To strike a balance between visual quality and application, we propose a heuristic invertible network for underwater perception enhancement, dubbed HUPE, which enhances visual quality and demonstrates flexibility in handling other downstream tasks. Specifically, we introduced an information-preserving reversible transformation with embedded Fourier transform to establish a bidirectional mapping between underwater images and their clear images. Additionally, a heuristic prior is incorporated into the enhancement process to better capture scene information. To further bridge the feature gap between vision-based enhancement images and application-oriented images, a semantic collaborative learning module is applied in the joint optimization process of the visual enhancement task and the downstream task, which guides the proposed enhancement model to extract more task-oriented semantic features while obtaining visually pleasing images. Extensive experiments, both quantitative and qualitative, demonstrate the superiority of our HUPE over state-of-the-art methods. The source code is available at https://github.com/ZengxiZhang/HUPE.