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.
