Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach

By Kede Ma, Hui Li, Hongwei Yong, Zhou Wang, Deyu Meng, Lei Zhang. IEEE Transactions on Image Processing, 2017

323 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity (2020, methodology): “They are used in target detection, HDTV, medical diagnosis and other ﬁelds (Ma, Ma, and Li 2019; Ma et al. 2017; Xing et al. 2018).”
Low-Light Image Enhancement With Semi-Decoupled Decomposition (2020, background): “Ma et al. [16] designed a multiple exposure fusion model, which fuses images at the patch level.”
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images (2018, methodology): “13 state-of-the-art MEF and HDR algorithms are employed in this process, including 8 MEF methods: Mertens09 [13], Raman09 [40], Shen11 [41], Zhang12 [42], Li13 [12], Shen14 [43], Ma17 [2], Kou17 [44], and 5 stack-based HDR methods: Sen12 [14], Hu13 [32], Bruce14 [45], Oh15 [15], Photomatix [46].”
SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer (2022, methodology): “The task-speciﬁc alternatives for MEF are SPD-MEF [41], MEFNet [83], and MEF-GAN [25].”