针对低光度条件下长曝光图像与短曝光图像的融合问题, 提出了一种基于块匹配的图像融合方法, 将低光度图像对融合成为一张更好的图像.首先提出基于金字塔多尺度模型的双向鬼影检测算法, 该算法降低了噪声和欠曝对鬼影检测的影响, 用于准确检测低光度场景中图像对之间由于物体运动造成的不一致区域; 随后利用块匹配算法重建鬼影区域; 最后运用改善边缘特性的泊松融合算法提取两次曝光图像中的信息生成结果图像.实验结果表明, 该方法有效地保留了短曝光图像的边缘锐度, 并保持了长曝光图像的色彩、亮度和细节.

Aiming at the fusion problem of long-exposure image and short-exposure image under low-light condition, a patchmatch-based method is proposed to fuse low-light image pair into a better one. Firstly, a bi-directional ghost detection scheme based on pyramid multi-scale algorithm is proposed, which reduces the influence of noise and under-exposure on ghost detection, and is used to accurately detect the inconsistent region caused by the motion of the object between the image pairs.Then, the patchmatch algorithm is used to reconstruct the ghost region. And finally the Poisson fusion algorithm of improved edge characteristics is used to extract the information from the two exposures. Experimental results show that the proposed method effectively preserves sharp outlines of the short-exposed image, and maintains the color, brightness and details of the long-exposed image.