Low Lumination Image Enhancement with Transformer based Curve Learning

• Yulin Cao,
• Chunyu Li,
• Guoqing Zhang,
• Yuhui Zheng,

Vol. 18, No. 9, September 30, 2024
10.3837/tiis.2024.09.009, Download Paper (Free):

• Enhancement
• Transformer
• Low lumination
• spatial attention
• channel attention
• convolutional neural network.

Abstract

Images taken in low lamination condition suffer from low contrast and loss of information. Low lumination image enhancement algorithms are required to improve the quality and broaden the applications of such images. In this study, we proposed a new Low lumination image enhancement architecture consisting of a transformer-based curve learning and an encoder-decoder-based texture enhancer. Considering the high effectiveness of curve matching, we constructed a transformer-based network to estimate the learnable curve for pixel mapping. Curve estimation requires global relationships that can be extracted through the transformer framework. To further improve the texture detail, we introduced an encoder-decoder network to extract local features and suppress the noise. Experiments on LOL and SID datasets showed that the proposed method not only has competitive performance compared to state-of-the-art techniques but also has great efficiency.

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