Reimplementation of the paper "D. Min, S. Choi, J. Lu, B. Ham, K. Sohn, and M. N. Do, Fast Global Image Smoothing Based on Weighted Least Squares, IEEE Trans. on Image Processing, 23(12), 5638-5653, 2014"
Two demo codes in MATLAB and C are provided.
output_image = FastGlobalSmoothing(input_image, sigma, lambda)
- The input image can be one of these types: uint8, uint16, single or double.
- The output image has the same size and data type as the input image.
- If sigma value is negative or zero, then an adaptive strategy based on local noise variance estimation will be adopted.
- The binary MEX files for the operation system Linux 64-bit and Windows 64-bit are provided, with extensions mexa64 and mexw64 respectively.
int FastGlobalSmoothing(float* image, int width, int height, float sigma, float lambda, int solver_iteration = 3)
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The elements of single channel data image are arranged in row-major order.
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The output image buffer overwrites the input image buffer.
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The value of input image is assumed to be in the range [0, 1].
Copyright (c) 2020, Li Chen All rights reserved.
For research and education purpose only.