Combination of Dark-channel Prior with Sparse Representation for Underwater Image Restoration
WANG Xin① ZHU Hangcheng① NING Chen② LÜ Guofang①
①(College of Computer and Information, Hohai University, Nanjing 211100, China) ②(School of Physics and Technology, Nanjing Normal University, Nanjing 210000, China)
Abstract:Due to the influences of scattering of the light and interference of the noise, underwater image quality is always degraded severely. In order to remove the blur and suppress the noise, and improve the quality of underwater image, a novel underwater image restoration method based on the combination of dark-channel prior with sparse representation is proposed. This method adopts the dark-channel prior theory to calculate the dark-channel image at first, and then uses sparse representation to denoise and optimize the dark-channel image. Based on the improved dark-channel image, the more precise water transmissivity and light intensity can be achieved to compute the final restoration result, effectively eliminating the image blur as well as noise. The experimental results show that the proposed method can effectively improve the image factors, such as average gradient and entropy, so as to compensate the degraded image.
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