Evaluation of focus measures in multi-focus image fusion

By Zhongliang Jing. Pattern Recognition Letters, 2007

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Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain (2019, result): “In comparison to the original modified Laplacian (ML) [41],”
Multi-focus image fusion with a deep convolutional neural network (2017, methodology): “The spatial domain methods in the early stage usually adopt a block-based fusion strategy, in which the source images are decomposed into blocks and each pair of block is fused with a designed activity level measurement like spatial frequency and sum-modiﬁed-Laplacian [17] .”
Analysis of focus measure operators for shape-from-focus (2013, methodology): “Energy of Laplacian (LAP1) The energy of the second derivative of the image has been used as a focus measure for both autofocus [6, 8, 19, 55, 59, 63, 64, 66, 67, 69, 70] and SFF [61]:”
Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain (2013, background): “Therefore , the directive contrast is integrated with the sum-modified-Laplacian [32] to get more accurate salient features.”
Simultaneous image fusion and denoising with adaptive sparse representation (2015, background): “The basic principle is to select image blocks or regions from source images with a certain activity level measurement [6].”