Updated 28 Dec 2020
The algorithm of image fusion using WLS is described in the following steps:
(1) A first step, in our algorithm, is two-scale decomposition based on Anisotropic Diffusion (ANI) which is used to separate coarser details (base layer) and finer details (detail layer) across each input exposure.
(2) Weak texture details (i.e. detail layer computed from ANI) and saturation measure are utilized to generate weight mask for controlling the contribution of pixels from base layers separated across all the multiple exposures.
(3) Weighted Least Squares (WLS) and sigmoid function based weight map refinement is performed for coarser details and finer details computed in the first step, respectively.
(4) Weighted average based blending of coarser details and finer details are performed to form a composite seamless image without blurring or loss of detail near large discontinuities.
Harbinder Singh (2021). Weighted Least Squares Based Detail Enhanced Exposure Fusion (https://github.com/Harbinder-fusion/Fusion-WLS/releases/tag/v1.0), GitHub. Retrieved .
Inspired by: Ghost-free multi exposure image fusion technique using dense, Multi-exposure and Multi-focus Image Fusion in Gradient Domain, Multi-Exposure Image Fusion Based on Illumination Estimation, Fusion of MRI and CT images using guided image filter and image statistics, Fusion of Infrared and Visible Sensor Images Based on Anisotropic Diffusion and KL Transform
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