Galdran, AdrianVazquez-Corral, JavierPardo, DavidBertalmío, Marcelo2016-06-102016-06-102015Galdran A, Vazquez-Corral J, Pardo D, Bertalmío M. Enhanced variational image dehazing. SIAM J Imaging Sci. 2015;8(3):1519-46. DOI: 10.1137/15M10088891936-4954http://hdl.handle.net/10230/26896Images obtained under adverse weather conditions, such as haze or fog, typically/nexhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling/nthe image structure under the haze layer and recovering vivid colors out of a single image/nremains a challenging task, since the degradation is depth-dependent and conventional methods are/nunable to overcome this problem. In this work, we extend a well-known perception-inspired variational/nframework for single image dehazing. Two main improvements are proposed. First, we replace/nthe value used by the framework for the grey-world hypothesis by an estimation of the mean of/nthe clean image. Second, we add a set of new terms to the energy functional for maximizing the/ninter-channel contrast. Experimental results show that the proposed Enhanced Variational Image/nDehazing (EVID) method outperforms other state-of-the-art methods both qualitatively and quantitatively./nIn particular, when the illuminant is uneven, our EVID method is the only one that recovers/nrealistic colors, avoiding the appearance of strong chromatic artifacts.application/pdfeng© Society for Industrial and Applied Mathematicsinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1137/15M1008889Image dehazingPerceptual color correctionContrast enhancementVariational image processingVisibility enhancementinfo:eu-repo/semantics/openAccess