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Weighted Least Squares Based Detail Enhanced Exposure Fusion

version 1.0 (5.63 KB) by Harbinder Singh
MATLAB Code implements the approach described in the paper "Weighted Least Squares Based Detail Enhanced Exposure Fusion"

6 Downloads

Updated 28 Dec 2020

From GitHub

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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.

Cite As

Harbinder Singh (2021). Weighted Least Squares Based Detail Enhanced Exposure Fusion (https://github.com/Harbinder-fusion/Fusion-WLS/releases/tag/v1.0), GitHub. Retrieved .

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MATLAB Release Compatibility
Created with R2020b
Compatible with any release
Platform Compatibility
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