Fractional-Order Retinex for Adaptive Contrast Enhancement of Under-Exposed Traffic Images

摘要: In this paper, a Fractional-order Retinex (FR) for the adaptive contrast enhancement of Under-Exposed Traffic Images (UETI) is proposed to be achieved by fractional-order variational method. The disposable reconstructive results UETI play significant role in traffic safety and are often taken as intermediate virtual reality augmented intelligent transportation systems. To end, paper proposes state-ofthe-art application promising mathematical method, fractional calculus, extend classic integer-order one, FR, which leads algebraic regularization term contributes better conditioning reconstruction problem. At first, isotropic equation related FR implemented Steepest Descent Method (FSDM). Secondly, corresponding restrictive optimization achieved. Finally, capability non-linearly preserve complex textural details well desired enhancing validated experimental analysis, major advantage superior conventional algorithms, especially rich details. gives novel approach, family algorithms that differs from most previous approaches such, it represents an interesting theoretical contribution.