Detail-preserving multi-scale exposure fusion via edge-preserving smoothing pyramids

Multi-scale exposure fusion is an effective way to directly fuse low dynamic range (LDR) image with different exposures into a content-rich LDR image for high dynamic range (HDR) reconstruction. Previous researches have shown that edge-preserving smoothing can be used to improve multi-scale exposure fusion. However, multi-scale exposure fusion via edge-preserving smoothing pyramids suffers from loss of details. To address this issue, we propose a side window gradient guided image filtering (SGGIF) and use it to construct an edge-preserving smooth pyramid. First, by adding eight kernels to the gradient guided image filtering(GGIF), a SGGIF with effective edge preserving is developed. Furthermore, we select the weight map with the minimum mean as the guidance image, which can further preserve details in the brightest and darkest regions of HDR scenes. Finally, we developed a detail-preserving multi-scale exposure fusion method based on edge-preserving smooth pyramids. Experimental results indicate that our method can effectively preserve details and reduce halo artifacts. Both quantitative and qualitative analyses demonstrate the effectiveness of our proposed approach.