Single Low-Light Ghost-Free Image Enhancement via Deep Retinex Model

2021 KSII Transactions on Internet and Information Systems  
Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination
more » ... ction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from lowresolution. To reduce the noise of the enhanced image and improve the image quality, a superresolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced. Keywords : image enhancement, low-light image, Retinex model, image decomposition, super-resolution Recently, deep learning-based approaches have achieved a remarkable traction, which also motivate the development of new deep learning-based approaches[18-21] for low-level image processing tasks, including super-resolution[22, 23], rain removal[24, 25], hyperspectral image[26, 27], and so on. Shen et al.[28] proposed the MSR-net to enhance the low-light image by learning a mapping from the low-light images to normal-light images. Chen et al. [29] introduced a low-light raw images dataset(SID) and developed an algorithm for processing these low-light raw images based on a deep learning network. Inspired by Retinex theory and deep learning, in this paper, a novel deep learning approach is proposed, which learns a mapping from illumination to illumination based on the Retinex theory, to enhance the low-light images. To improve the quality and dynamic range of the image, a super-resolution algorithm is introduced based on the Laplacian pyramid network[30] to optimize the enhanced image. Besides, combination loss function is introduced during the training. We analyze the performance of the proposed algorithm with different datasets and different algorithms, such as Histogram Equalization(HE), Gray World Algorithm (GWA), Automatic White Balance (AWB), Gamma adjustment ( GA), Dong et al.[31], lowlight enhancement methods LIME[32], RetinexNet[33], and KinD[34]. Several distinct metrics are utilized to evaluate and compare the enhanced image, such as Peak Signal-to-Noise Ratio (PSNR), Entropy, Structural SIMilarity(SSIM), Natural image quality evaluator (NIQE)[35], Perception based Image Quality Evaluator (PIQE)[36], and Runtime. The experimental results show that the proposed algorithm achieves superior performance than existing methods. We highlight our main contributions as follows: • A new extreme low-light image dataset with 1331 images are constructed for the network training, which consists of three parts: 347 images of the LOL[33] dataset, 735 images of the SICE[37], and 249 images are captured by our camera, each lowlight image has a corresponding normal-light image.
doi:10.3837/tiis.2021.05.013 fatcat:jryiqhgo3rdctooipj6awsn3c4