A predictive intelligence approach for low-light enhancement

Abstract

Images captured in low-light scenes often suffer from poor contrast, severe loss of details and a lot of noise. These issues mentioned above can degrade the performance of some computer vision tasks such as object detection and recognition. The Retinex theory, which decomposes low-light images into reflection and illumination components, is an effective tool for predicting high-light images. Most Retinex-based methods cannot deal with noise reduction and over-smoothing simultaneously. In this paper, we propose a predictive intelligence approach of a multi-task framework to enhance the low-light image. The framework is composed of four coarse recovery sub-networks and three cross refinement sub-networks. Firstly, we use the LIME method to pre-process the low-light image and input the results to the coarse recovery sub-networks as priori knowledges along with the original low-light image. The residual connection and mixed-domain attention module are embedded in each coarse recovery sub-network so that the feature map processed by the attention mechanism is merged with the feature map from the main branch to improve the ability of predicting local bright color patches and effectively control the number of noises. The refinement sub-networks take the output of the coarse sub-networks as input and feed them into U-Net architectures in cross manner. The refinement sub-networks establish a multi-task framework with a cross-task distillation module that can significantly improve the predictive ability of the whole network. Extensive experiments demonstrate that our method achieves 4.1 in MOS, 19.82 in PSNR, 0.8470 in SSIM, 3.0271 in NIQE, and improves by more than 5% in both subjective metrics and objective metrics compared to state-of-the-art alternatives.