A Multi-image Local Structured Fusion-based Low-light Image Enhancement Algorithm

To combine the complementary information of the multi-exposure images generated from a given low-light image, a two-stage low-light image enhancement (LLIE) algorithm adopting multi-image local structural fusion approach was proposed to produce a fused image that is more informative and robust than each one. Specifically, in the image preparation stage, an optimal exposure prediction model based on image quality assessment was first built. Then a well-exposed image and an over-exposed image were generated with corresponding exposure ratios estimated with the prediction model for a given low-light image, respectively. Simultaneously, the classical Retinex model was used to obtain another well-exposed image to provide more supplementary information to be fused. In the fusion stage, the patches extracted from the low-light image, two well-exposed images, and the over-exposed image at the same spatial positions were vectorized and decomposed into independent components, i.e., contrast, texture structure, and brightness. The desired contrast of the fused image patch was determined by the highest contrast of all source image patches, while the structural strength and brightness components were weighted with phase congruency map and visual saliency map, respectively. Upon fusing these three components separately, we reconstructed a desired patch and placed it back into the fused image. Finally, a denoising algorithm whose input parameter is estimated by a noise level estimation algorithm was exploited to suppress the accompanying noise due to enhancement process. The experimental results show that, the proposed LLIE algorithm outperforms the existing the state-of-art ones in the terms of both subjective and objective image quality assessment. © 2022 Science Press. All rights reserved.