Underwater image enhancement based on color restoration and dual image wavelet fusion

Due to the severe light absorption and scattering, underwater images often exhibit problems such as low contrast, detail blurring, color attenuation, and low illumination. To address these issues, this paper presents a two-step strategy based on color restoration and image fusion by combining deep learning and conventional image enhancement technologies to improve the visual performance of underwater images. First, an adaptive color compensation method is proposed to make up for the loss of severely attenuated channels. Color restoration is further implemented to estimate the illuminant color cast caused by the selective attenuation of light. Since the underwater image after color restoration still suffers from scattering and blurring, an effective method based on dual image wavelet fusion (DIWF) and Generative Adversarial Network (GAN) is designed to further enhance the edge details and improve the contrast of the color restored image. Experiments demonstrate that the proposed method significantly outperforms several state-of-the-arts in both qualitative and quantitative qualities. The approach can achieve better performance of color restoration, blur removal, and low illumination enhancement.

Automated summary

This paper presents a two-step strategy based on deep learning and conventional image enhancement technologies to improve the visual performance of underwater images. The method combines color restoration and image fusion to address issues such as low contrast and detail blurring, achieving significantly better results in both qualitative and quantitative qualities.