Deep Underwater Image Restoration and Beyond

Abstract

Underwater image restoration is a challenging problem due to the multiple distortions. Degradation in the information is mainly due to the 1) light scattering effect 2) wavelength dependent color attenuation and 3) object blurriness effect. In this letter, we propose a novel end-to-end deep network for underwater image restoration. The proposed network is divided into two parts viz. channel-wise color feature extraction module and dense-residual feature extraction module. A custom loss function is proposed, which preserves the structural details and generates the true edge information in the restored underwater scene. Also, to train the proposed network for underwater image enhancement, a new synthetic underwater image database is proposed. Existing synthetic underwater database images are characterized by light scattering and color attenuation distortions. However, object blurriness effect is ignored. We, on the other hand, introduced the blurring effect along with the light scattering and color attenuation distortions. The proposed network is validated for underwater image restoration task on real-world underwater images. Experimental analysis shows that the proposed network is superior than the existing state-of-the-art approaches for underwater image restoration.