DarkIR: Robust Low-Light Image Restoration
Daniel Feijoo, Juan Cruz-Benito, Alvaro Garcia, Marcos V. Conde
Abstract
Photography during night or in dark conditions typically suffers from noise, low-light and blurring issues due to the dim environment and the common use of long exposure. Although Deblurring and Low-light Image Enhancement (LLIE) are related under these conditions, most approaches to image restoration solve these tasks separately. In this paper, we present an efficient and robust neural network for multi-task low-light image restoration. Instead of following the current tendency of Transformer-based models, we propose new attention mechanisms to enhance the receptive field of efficient CNNs. Our method reduces the computational costs in terms of parameters and MAC operations compared to previous methods. Our model, DarkIR, achieves new state-of-the-art results on the popular LOL-Blur, LOLv2 and Real-LOLBlur datasets, being able to generalize on real-world night and dark images.