TCPCNet: a transformer-CNN parallel cooperative network for low-light image enhancement

Recently, deep learning has made impressive achievements in low-light image enhancement. Most existing deep learning-based methods use convolutional neural networks (CNN) by stacking network depth and modifying network architecture to improve feature extraction capabilities and restore degraded images. However, these methods have obvious defects. Although CNN excels at extracting local features, its small receptive field is unable to capture the global brightness, leading to overexposure. The Transformer model from natural language processing has recently produced positive outcomes in a variety of computer vision issues thanks to its excellent global modeling capabilities. However, its complex modeling method makes it difficult to capture local details and takes up many computing resources, making it challenging to apply to the enhancement of low-light images, especially high-resolution images. Based on deep convolution and Transformer characteristics, this paper proposes a Transformer-CNN Parallel Cooperative Network (TCPCNet), which supplements image details and local brightness while ensuring global brightness control. We also changed the calculation method of the traditional Transformer to be applied to enhance high-resolution low-light images without affecting performance. Extensive experiments on public datasets show that the proposed TCPCNet achieves comparable performance against the state-of-the-art approaches.