Human visual characteristics inspired high-efficiency exposure selection for HDR imaging.

In high dynamic range (HDR) imaging, exposure parameters determine the efficiency of image acquisition and have a great impact on image quality. Two exposure selection methods are proposed to improve the efficiency of HDR imaging while maintaining image quality. The target signal-to-noise ratio (SNR) is derived from the contrast sensitivity of the human visual system under a given adaptation condition. Combining the target SNR and imaging model, a histogram-independent iterative method and a histogram-based exhaustive method are developed to determine the exposure time set. The two methods are complementary: the former offers low computational cost, while the latter enhances control over exposure parameters and improves image acquisition efficiency. An HDR dataset with 1/3-stop exposure intervals and diverse illumination conditions is established, which is utilized alongside a publicly available HDR dataset to evaluate the proposed methods in comparison with existing methods. The proposed methods achieve high quality while reducing image capture time, demonstrating the superior efficiency of HDR image acquisition.