一种多图像局部结构化融合的低照度图像增强算法EI北大核心CSCD

为将低照度图像及基于它生成的多个不同曝光度图像中的互补性信息进行最佳融合以获得更为鲁棒的视觉增强效果,提出了一种基于多图像局部结构化融合的两阶段低照度图像增强(Low-light image enhancement,LLIE)算法.在待融合图像制备阶段,提出了一种基于图像质量评价的最佳曝光度预测模型,利用该预测模型给出的关于低照度图像最佳曝光度值,在伪曝光模型下生成适度增强图像和过曝光图像(利用比最佳曝光度值更高的曝光度生成)各一幅.同时,利用经典Retinex模型生成一幅适度增强图像作为补充图像参与融合.在融合阶段,首先将低照度图像、适度增强图像(2幅)和过曝光图像在同一空间位置处的图块矢量化后分解为对比度、结构强度和亮度三个分量.之后,以所有待融合对比度分量中的最高值作为融合后的对比度分量值,而结构强度和亮度分量则分别以相位一致性映射图和视觉显著度映射图作为加权系数完成加权融合.然后,将分别融合后的对比度、纹理结构和亮度三个分量重构为图块,并重新置回融合后图像中的相应位置.最后,在噪声水平评估算法导引下自适应调用降噪算法完成后处理.实验结果表明:所提出的低照度图像增强算法在主客观图像质量评价上优于现有大多数主流算法.

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.