The problem of restoring images degraded by an underwater environment is challenging, in part because light traveling underwater suffers from two combined degradations, known as scattering and absorption, which leads to inaccurate transmittance estimation. In this work, we propose that underwater image dehazing and color correction algorithm based on scene depth estimation. Through scene depth estimation, we get accurate transmittance to achieve better dehazing effect. The experimental results show that our approach obtains good-quality images, with a visibility enhancement comparable or better than other recent methods. As for color recovery, We recovere among different images, regardless of the different water conditions. In this work we not only achieves the effect of underwater image dehazing, but also guarantees accuracy and timeliness of recovery results.
At present, the restoration of turbulent degraded images is a worldwide problem in the fields of astronomical imaging. Atmospheric turbulence is the reason why images will be blurred, which gravely interferes the object of recognition and the detection of images. This paper presents a restoration method for the turbulent degraded images based on the image saliency edge selection and the L0 norm constraint, which aims to recover sharp images from the turbulent degraded images. The proposed method imposes the L0 norm sparse constraint on the latent image, and uses the method of split Bregman to solve the problem of optimization. To avoid the influence of the tiny details on the point spread function (PSF) , we use the image saliency algorithm to build a weighted model to select salient edges from the latent images. Based on the salient edge in the gradient domain, the proposed method establishes an estimation model of the point spread function. The calculation part of the point spread function is solved accurately by using the fast Fourier transform (FFT) in the frequency domain. The proposed method uses the multi-scale pyramid strategy to alternatively solve the point spread function and the latent images, which can obtain the final accuracy of the point spread function.
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