Multi-Exposure Fusion of Gray Images Under Low Illumination Based on Low-Rank Decomposition

Abstract

Existing multi-exposure fusion (MEF) algorithms for gray images under low-illumination cannot preserve details in dark and highlighted regions very well, and the fusion image noise is large. To address these problems, an MEF method is proposed. First, the latent low-rank representation (LatLRR) is used on low-dynamic images to generate low-rank parts and saliency parts to reduce noise after fusion. Then, two components are fused separately in Laplace multi-scale space. Two different weight maps are constructed according to features of gray images under low illumination. At the same time, an energy equation is designed to obtain the optimal ratio of different weight factors. An improved guided filtering based on an adaptive regularization factor is proposed to refine the weight maps to maintain spatial consistency and avoid artifacts. Finally, a high dynamic image is obtained by the inverse transform of low-rank part and saliency part. The experimental results show that the proposed method has advantages both in subjective and objective evaluation over state-of-the-art multi-exposure fusion methods for gray images under low-illumination imaging.

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

“…Regardless, it can be used as an evaluation reference. Huang [1]; Yang [13]; MEF-GAN [17]; Liu [58]; Yang [62]; Martorell [64]; Li [77]; Liu [80]; Chen [81]; Deepfuse [84]; MEFNet [86]; U2fusion [88]; Gao [89]; LXN [123]; Shao [134]; Wu [135]; Merianos [136] The larger, the better 2 Q AB/F Nie [6]; Liu [38]; LST [42]; Hayat [115]; Shao [134] The larger, the better 3 MEF-SSIMc Martorell [64]; UMEF [87]; Shao [134] The larger, the better 4 Mutual information (MI) Nie [6]; Wang [34]; Gao [89]; Choi [137] The larger, the better 5 Peak signal-to-noise ratio (PSNR) Kim [7]; MEF-GAN [17]; Chen [81]; U2fusion [88]; Gao [89]; Shao [134] The larger, the better 6 Natural image quality evaluator (NIQE) Huang [1]; Hayat [115]; Wu [135]; Xu [138] The smaller, the better 7 Standard deviation (SD) MEF-GAN [17]; Gao [89]; Wu [135] The larger, the better 8 Entropy (EN) Gao [89]; Wu…”

“…The main difference between these image fusion tasks is that the source images are different, and the source images of MEF are a series of images with different exposure levels. In addition, it can also be used for image enhancement under low illumination [6,7], defogging [8], and saliency detection [9] by fusing or generating pseudo exposure sequences.…”

Enhancement and Noise Suppression of Single Low-Light Grayscale Images

“…Low-light images typically have many noises. To improve the signal-to-noise ratio (SNR) of the enhanced image, LatLRR [28,29] was utilized to decompose the source image…”

“…Low-light images typically have many noises. To improve the signal-to-noise ratio (SNR) of the enhanced image, LatLRR [28,29] was utilized to decompose the source image X src and the intermediate image X vir . LatLRR is efficient and robust to noise and outliers.…”

An Infrared and Visible Image Fusion Method Guided by Saliency and Gradient Information

“…In addition, these methods can fuse infrared and visible image without complex training process and good performance GPUs. Thus, fusion methods based on LatLRR are widely used in image fusion [14,24,25].…”