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.
Highlights
Low-illumination imaging can compensate for problems in information collection in low-illumination environments, improving national defense ability
Multi-exposure fusion of gray images under low illumination faces two challenges: images under low illumination have low contrast and relatively large noise resulting in a lower signal-to-noise ratio of the fusion image; and compared with color images, gray images have less information available for building fusion weight maps resulting in detail loss in the dark and highlighted regions
The image is decomposed by latent low-rank representation (LatLRR) and the image is fused by inverse transformation in this paper
Summary
Low-illumination imaging can compensate for problems in information collection in low-illumination environments, improving national defense ability. An MEF framework based on low-level image features and image patch structure decomposition was proposed to improve the robustness of ghost removal in a dynamic scene, and preserved more detailed information [30] Overall, this method mainly pays attention to the block segmentation. Multi-exposure fusion of gray images under low illumination faces two challenges: images under low illumination have low contrast and relatively large noise resulting in a lower signal-to-noise ratio of the fusion image; and compared with color images, gray images have less information available for building fusion weight maps resulting in detail loss in the dark and highlighted regions Given these problems, a novel approach based on latent low-rank representation (LatLRR) and adaptive weights is proposed.
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