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• W2735958603 abstract "This paper presents a novel variational framework for low-light image enhancement. The proposed enhancement algorithm simultaneously performs brightness enhancement and noise reduction using a variational optimization. An edge-preserved noise reduction is performed by minimizing the total variation constraint term in the energy function. In addition, the proposed method estimates the optimal transmission map to restore the low-light image by minimizing the ℓ2-norm smoothness and data-fidelity terms. To minimize the proposed energy functional, the proposed method splits the ℓ1-derivative term under the split Bregman iteration framework. The performance of the proposed method is evaluated using both simulated and natural low-light images. Experimental results show that the proposed enhancement method can significantly improve the quality of the low-light images without noise amplification." @default.
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• W2735958603 date "2017-10-01" @default.
• W2735958603 modified "2023-10-03" @default.
• W2735958603 title "Variational framework for low-light image enhancement using optimal transmission map and combined<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML altimg=si68.gif display=inline id=mml68 overflow=scroll><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>and<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML altimg=si69.gif display=inline id=mml69 overflow=scroll><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:…" @default.
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• W2735958603 doi "https://doi.org/10.1016/j.image.2017.06.016" @default.
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