Exposure Fusion Algorithm Based on Perceptual Contrast and Dynamic Adjustment of Well-Exposedness

The luminance of natural scenes frequently presents a high dynamic range and cannot be adequately captured with traditional imaging devices. Additionally, even if the technology to capture the scene is available, the image to be displayed on conventional monitors must be compressed by a tone mapping operator. Exposure fusion is an affordable alternative which blends multiple low dynamic range images, taken by a conventional camera under different exposure levels, generating directly the display image. In this paper, the Retinal-like Sub-sampling Contrast metric has been adapted to work with the original version of the exposure fusion algorithm in the CIELAB color space. In addition, saturation and well-exposedness metrics have been reformulated in this color space, adding a dynamic adjustment mechanism to the latter one which avoids amplification of invisible contrast. Results based on objective evaluation show that the proposed algorithm clearly outperforms the original exposure fusion technique and most of the state-of-the-art tone mapping operators for static images.