Image Restoration Based on Combined Dark Channel and Image Registration Due to the influence of water surface fluctuation and water turbulence, underwater target imaging detection technology faces great difficulties. This paper proposes an image restoration method combining dark channel prior theory and image registration technology, mainly using Bspline based nonrigid registration framework to correct image distortion. Since the conventional image registration algorithm often uses the average image of the sequence as a reference image, the average image tends to have severe blurring, which reduces the accuracy of registration. Therefore, this paper proposes to use the dark channel prior theory for the deblurring of the average image, and then use the deblurred average image as a reference for image registration. The test results show that the method can effectively improve the imaging distortion caused by surface turbulence.

Underwater Image Enhancement Based on the Iteration of a Generalization of Dark Channel Prior Underwater image enhancement is important for images captured in underwater because underwater images often suffer from color cast, low contrast and degraded visibility due to the absorption and scattering of light in water. In this paper, we propose a novel algorithm for underwater image restoration based on a generalization of the dark channel prior (GDCP). Though there are various types of underwater images, we especially focus on underwater images with depth because these images are not enhanced well by current algorithms. The proposed algorithm is composed of the iteration of GDCP and image fusion. Additionally, we introduce the new ambient light estimation to adapt to more types of images. Experimental results show that proposed algorithm is effective for various types of underwater images, especially for the images with depth.