Underwater images present blur and color cast, caused by light absorption and scattering in water medium. To restore underwater images through image formation model (IFM), the scene depth map is very important for the estimation of the transmission map and background light intensity. In this paper, we propose a rapid and effective scene depth estimation model based on underwater light attenuation prior (ULAP) for underwater images and train the model coefficients with learning-based supervised linear regression. With the correct depth map, the background light (BL) and transmission maps (TMs) for R-G-B light are easily estimated to recover the true scene radiance under the water. In order to evaluate the superiority of underwater image restoration using our estimated depth map, three assessment metrics demonstrate that our proposed method can enhance perceptual effect with less running time, compared to four state-of-theart image restoration methods.
Light absorption and scattering lead to underwater image showing low contrast, fuzzy, and color cast. To solve these problems presented in various shallow-water images, we propose a simple but effective shallow-water image enhancement method-relative global histogram stretching (RGHS) based on adaptive parameter acquisition. The proposed method consists of two parts: contrast correction and color correction. The contrast correction in RGB color space firstly equalizes G and B channels and then redistributes each R-G-B channel histogram with dynamic parameters that relate to the intensity distribution of original image and wavelength attenuation of different colors under the water. The bilateral filtering is used to eliminate the effect of noise while still preserving valuable details of the shallow-water image and even enhancing local information of the image. The color correction is performed by stretching the 'L' component and modifying 'a' and 'b' components in CIE-Lab color space. Experimental results demonstrate that the proposed method can achieve better perceptual quality, higher image information entropy, and less noise, compared to the state-of-the-art underwater image enhancement methods.
Density functional theory calculations have been carried out for CO adsorption on the Fe(oct2)- and Fe(tet1)-terminated Fe(3)O(4)(111) surfaces, which are considered as active catalysts in water-gas shift reaction. It is found that the on-top configurations are most stable on these two surfaces. Some bridge configurations are also stable in which the new C-O bond formed between the surface O atom and the C atom of CO. The adsorption on the Fe(oct2)-terminated surface is more stable than on the Fe(tet1)-terminated surface. The density of state reveals the binding mechanism of CO adsorption on the two surfaces. Our calculations have also shown that the absorbed CO can migrate from the on-top site to the bridge site or 3-fold site. The oxidation of CO via surface oxygen atoms is feasible, which is in good agreement with experimental results.
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