Abstract
Guided by the Retinex model, image decomposition based low-light image enhancement methods attempt to manipulate the estimated illumination and project it back to the corresponding reflectance. However, the L2 constraint on the illumination often leads to halo artifacts, and the noise existed in the reflectance map is always neglected. In this paper, based on the Retinex model, we introduce a total variation optimization problem that jointly estimates noise-suppressed reflectance and piece-wise smooth illumination. The gradient of the reflectance is also constrained so that the contrast of the final enhancement result can be strengthened. Experimental results demonstrate the effectiveness of the proposed method with respect to low-light image enhancement.
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