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 in 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 LOLBlur, LOLv2 and Real-LOLBlur datasets, being able to generalize on real-world night and dark images. Code and models at https://github.com/cidautai/DarkIR

翻译：在夜间或黑暗条件下拍摄的照片，由于环境昏暗且通常采用长曝光，常受到噪声、光照不足和模糊等问题的困扰。尽管在这些条件下，去模糊与低光照图像增强（LLIE）具有关联性，但图像复原领域的大多数方法仍分别处理这些任务。本文提出一种高效且鲁棒的神经网络，用于多任务低光照图像复原。不同于当前基于Transformer模型的趋势，我们提出了新的注意力机制以增强高效CNN的感受野。与先前方法相比，我们的方法在参数量和MAC运算量方面显著降低了计算成本。我们的模型DarkIR在主流数据集LOLBlur、LOLv2和Real-LOLBlur上取得了新的最优性能，并能够泛化至真实世界的夜间与黑暗场景图像。代码与模型发布于https://github.com/cidautai/DarkIR。