Variational Low-Light Image Enhancement Based on Fractional-Order Differential (Communications in Computational Physics, 2024)
Images captured under insufficient light conditions often suffer from noticeable degradation of visibility, brightness and contrast. Existing methods pose limitations on enhancing low-visibility images, especially for diverse low-light conditions.In this paper, we first propose a new variational model for estimating the illuminationmap based on fractional-order differential. Once the illumination map is obtained,we directly inject the well-constructed illumination map into a general image restoration model, whose regularization terms can be viewed as an adaptive mapping. Sincethe regularization term in the restoration part can be arbitrary, one can model theregularization term by using different off-the-shelf denoisers and do not need to explicitly design various priors on the reflectance component. Because of flexibility ofthe model, the desired enhanced results can be solved efficiently by techniques likethe plug-and-play inspired algorithm. Numerical experiments based on three publicdatasets demonstrate that our proposed method outperforms other competing methods, including deep learning approaches, under three commonly used metrics in termsof visual quality and image quality assessment.
Keywords: Low-light image; image enhancement; fractional-order; variational methods.