Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method
This work addresses the need for efficient image processing in high-resolution low-light conditions, providing a benchmark and method that enhance performance for applications such as surveillance or photography.
The paper tackles low-light image enhancement for ultra-high-definition images by introducing a large-scale 4K/8K benchmark and a transformer-based method called LLFormer, which outperforms state-of-the-art methods on this dataset and improves downstream tasks like face detection.
As the quality of optical sensors improves, there is a need for processing large-scale images. In particular, the ability of devices to capture ultra-high definition (UHD) images and video places new demands on the image processing pipeline. In this paper, we consider the task of low-light image enhancement (LLIE) and introduce a large-scale database consisting of images at 4K and 8K resolution. We conduct systematic benchmarking studies and provide a comparison of current LLIE algorithms. As a second contribution, we introduce LLFormer, a transformer-based low-light enhancement method. The core components of LLFormer are the axis-based multi-head self-attention and cross-layer attention fusion block, which significantly reduces the linear complexity. Extensive experiments on the new dataset and existing public datasets show that LLFormer outperforms state-of-the-art methods. We also show that employing existing LLIE methods trained on our benchmark as a pre-processing step significantly improves the performance of downstream tasks, e.g., face detection in low-light conditions. The source code and pre-trained models are available at https://github.com/TaoWangzj/LLFormer.