Enhance dim images using gamma correction with deep learning models with IAGC
Enhance dim images using gamma correction with deep learning models with IAGC
Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network
arXiv paper abstract https://arxiv.org/abs/2308.08220
arXiv PDF paper https://arxiv.org/pdf/2308.08220.pdf
... presents a novel network structure with illumination-aware gamma correction and complete image modelling to solve the low-light image enhancement problem.
... propose to integrate the effectiveness of gamma correction with the strong modelling capacities of deep networks, which enables the correction factor gamma to be learned in a coarse to elaborate manner via adaptively perceiving the deviated illumination.
Because exponential operation introduces high computational complexity, ... propose to use Taylor Series to approximate gamma correction, accelerating the training and inference speed.
Dark areas usually occupy large scales in low-light images, common local modelling structures, e.g., CNN, SwinIR, are thus insufficient to recover accurate illumination across whole low-light images.
... propose a novel Transformer block to completely simulate the dependencies of all pixels across images via a local-to-global hierarchical attention mechanism, so that dark areas could be inferred by borrowing the information from far informative regions in a highly effective manner.
... demonstrate that ... approach outperforms state-of-the-art methods.
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