Objective evaluation results of different algorithms on LOL datasets.

Objective evaluation results of different algorithms on LOL datasets.

<p>Objective evaluation results of different algorithms on LOL datasets.</p>

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Bibliographic Details
Main Author:	Mohan Yin (20721395) (author)
Other Authors:	Jianbai Yang (20721398) (author)
Published:	2025
Subjects:	Cell Biology Neuroscience Science Policy Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified six common datasets modules respectively expand iterative learning mechanisms iterative learning mechanism dilated convolution module demonstrating significant advantages convergent illumination estimation adaptive learning sub network &# 8217 image &# 8217 global feature attention rich color features color features simultaneously adaptively enhance features retinex decomposition sub clear reflection component reflection component global features feature loss features along dimensional features detailed features retinex theory retinex decomposition world environments ssim values receptive fields receptive field paper proposes overly focus often affected network focuses lol dataset light environments increasing brightness external factors enhanced image edge details different stages channel dimension capture multi
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Summary:	<p>Objective evaluation results of different algorithms on LOL datasets.</p>

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