The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter, but has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: it can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and non-approximate linear time algorithm, regardless of the kernel size and the intensity range. Currently it is one of the fastest edge-preserving filters.

Guided-image-filter-kaiming He
..............................\data
..............................\....\img_enhancement
..............................\....\...............\tulips.bmp
..............................\....\img_feathering
..............................\....\..............\toy-mask.bmp
..............................\....\..............\toy.bmp
..............................\....\img_flash
..............................\....\.........\cave-flash.bmp
..............................\....\.........\cave-noflash.bmp
..............................\....\img_smoothing
..............................\....\.............\cat.bmp
..............................\matlab_code
..............................\...........\boxfilter.m
..............................\...........\example_enhancement.m
..............................\...........\example_feathering.m
..............................\...........\example_flash.m
..............................\...........\example_smoothing.m
..............................\...........\guidedfilter.m
..............................\...........\guidedfilter_color.m
..............................\readme.txt

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