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.
This work was supported by National Natural Science Foundation of China under contract No. 61472011 and Microsoft Research Asia Project under contract No. FY17-RES-THEME-013.
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Li, M., Liu, J., Yang, W., Guo, Z. (2018). Joint Denoising and Enhancement for Low-Light Images via Retinex Model. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_9
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DOI: https://doi.org/10.1007/978-981-10-8108-8_9
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