Multiexposure image fusion using intensity enhancement and detail extraction

• Intensity image enhancement is achieved by CLAHE and homomorphic filtering.
• Guiled filtering and WLS optimization extract local and global details, respectively.
• Weighting maps are estimated by spatial and cross-image consistencies.

In this study, a multiexposure image fusion approach using intensity enhancement and detail extraction is proposed. The N input low dynamic range (LDR) RGB color images are transformed into HSI color space. Intensity enhancement is achieved by CLAHE and homomorphic filtering. Gamma correction is used to compensate the nonlinear response of display devices, whereas “cross-image” median filtering is used to generate the reference intensity image. L0 smoothing filter and weighted least squares (WLS) optimization are used to perform local and global detail extractions on the N processed LDR images, respectively. The N weighting maps of the N processed LDR images are estimated by spatial and cross-image consistencies and then refined by cross bilateral filtering. Finally, the multiresolution spline based scheme is used to perform multiexposure image fusion. Based on the experimental results obtained in this study, the performance of the proposed approach is better than those of four comparison approaches.

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