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- W2769041710 abstract "Low-light images often suffer from poor quality and low visibility. Improving the quality of low-light image is becoming a highly desired subject in both computational photography and computer vision applications. This paper proposes an effective method to constrain the illumination map t by estimating the norm and constructing the constraint coefficients, which called LieCNE. More specifically, we estimate the initial illumination map by finding the maximum value of R, G and B channels and optimize it by norm estimation. We propose a function ({t^gamma }) to contain the exponential power (gamma ) in order to optimize the enhancement effect under different illumination conditions. Finally, a new evaluation criterion is also proposed. We use the similarity with the true value to determine the enhanced effect. Experimental results show that LieCNE exhibits better performance under a variety of lighting conditions in enhancement results and image spillover prevention." @default.
- W2769041710 created "2017-12-04" @default.
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- W2769041710 date "2017-01-01" @default.
- W2769041710 modified "2023-09-26" @default.
- W2769041710 title "Low-Light Image Enhancement Based on Constrained Norm Estimation" @default.
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- W2769041710 doi "https://doi.org/10.1007/978-981-10-7299-4_30" @default.
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