수 학 과

제목: Image enhancement based on retinex theory

초록: In the first part, I introduce a novel variational model for the joint enhancement and restoration of dark images corrupted by blurring and/or noise. The model decomposes a given dark image into reflectance and illumination images that are recovered from blurring and/or noise. In addition, our approach utilizes non-convex total variation regularization on all variables. This allows us to adequately denoise homogeneous regions while preserving the details and edges in both reflectance and illumination images, which leads to clean and sharp final enhanced images.  Experimental results demonstrate the effectiveness of the proposed model when compared to other state-of-the-art methods in terms of both visual aspect and image quality measures. In the second part, I propose a novel variational model for the restoration of a single color image degenerated by haze. The model extends the total variation based model, by inserting an inter-channel correlation term. This additional term permits both color and gray-valued transmission maps, which enable broader applications of the proposed model. Numerical experiments validate the outstanding performance of the proposed model compared to the state-of-the-art methods.

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