Improved Image Selection for Stack-Based HDR Imaging
This work addresses a domain-specific challenge in computational photography for photographers and imaging professionals, offering an incremental improvement over existing methods.
The paper tackles the problem of selecting exposures for stack-based HDR imaging to reduce acquisition and processing time while ensuring full scene capture, proposing a fully automatic method that improves accuracy and speed, as shown by better performance on benchmark scenes using metrics like mean squared error and perception-based scores.
Stack-based high dynamic range (HDR) imaging is a technique for achieving a larger dynamic range in an image by combining several low dynamic range images acquired at different exposures. Minimizing the set of images to combine, while ensuring that the resulting HDR image fully captures the scene's irradiance, is important to avoid long image acquisition and post-processing times. The problem of selecting the set of images has received much attention. However, existing methods either are not fully automatic, can be slow, or can fail to fully capture more challenging scenes. In this paper, we propose a fully automatic method for selecting the set of exposures to acquire that is both fast and more accurate. We show on an extensive set of benchmark scenes that our proposed method leads to improved HDR images as measured against ground truth using the mean squared error, a pixel-based metric, and a visible difference predictor and a quality score, both perception-based metrics.