A Hybrid Framework for Curve Estimation Based Low Light Image Enhancement

Images captured in low-light conditions often suffer from poor visibility and noise corruption. Low-light image enhancement (LLIE) aims to restore the brightness of under-exposed images. However, most previous LLIE solutions enhance low-light images via global mapping without considering various degradations of dark regions. Besides, these methods rely on convolutional neural networks for training, which have limitations in capturing long-range dependencies. To this end, we construct a hybrid framework dubbed hybLLIE that combines transformer and convolutional designs for LLIE task. Firstly, we propose a light-aware transformer (LAFormer) block that utilizes brightness representations to direct the modeling of valuable information in low-light regions. It is achieved by utilizing a learnable feature reassignment modulator to encourage inter-channel feature competition. Secondly, we introduce a SeqNeXt block to capture the local context, which is a ConvNet-based model to process sequences of image patches. Thirdly, we devise an efficient self-supervised mechanism to eliminate inappropriate features from the given under-exposed samples and employ high-order curves to brighten the low-light images. Extensive experiments demonstrate that our HybLLIE achieves comparable performance to 17 state-of-the-art methods on 7 representative datasets.