Histoformer: Histogram-Based Transformer for Efficient Underwater Image Enhancement
Yan‐Tsung Peng, Yen-Rong Chen, G Chen, Chun-Jung Liao
Abstract
When taking images underwater, we often find they have low contrast and color distortions since light passing through water suffers from absorption, scattering, and attenuation, making it difficult to see the scene clearly. To address this, we propose an effective model for underwater image enhancement using a histogram-based transformer (Histoformer), learning histogram distributions of high-contrast and color-corrected underwater images to produce the desired histogram to improve the visual quality of underwater images. Furthermore, we integrate the Histoformer with a generative adversarial network for pixel-based quality refinement. Experimental results demonstrate that the proposed model performs favorably against state-of-the-art underwater image restoration and enhancement approaches quantitatively and qualitatively.