Open Collections

Computational single-image high dynamic range imaging

University of British Columbia

This thesis proposes solutions for increasing the dynamic range (DR)—the number of intensity levels—of a single image captured by a camera with a standard dynamic range (SDR). The DR in a natural scene is usually too high for SDR cameras to capture, even with optimum exposure settings. The intensity values of bright objects (highlights) that are above the maximum exposure capacity get clipped due to sensor over-exposure, while objects that are too dark (shades) appear dark and noisy in the image. Capturing a high number of intensity levels would solve this problem, but this is costly, as it requires the use of a camera with a high dynamic range (HDR). Reconstructing an HDR image from a single SDR image is difficult, if not impossible, to achieve for all imaging situations. For some situations, however, it is possible to restore the scene details, using computational imaging techniques. We investigate three such cases, which also occur commonly in imaging. These cases pose relaxed and well-posed versions of the general single-image high dynamic range imaging (HDRI) problem. The first case occurs when the scene has highlights that occupy a small number of pixels in the image; for example, night scenes. We propose the use of a cross-screen filter, installed at the lens aperture, to spread a small part of the light from the highlights across the rest of the image. In post-processing, we detect the spread-out brightness and use this information to reconstruct the clipped highlights. Second, we investigate the cases when highlights occupy a large part of the scene. The first method is not applicable here. Instead, we propose to apply a spatial filter at the sensor that locally varies the DR of the sensor. In post-processing, we reconstruct an HDR image. The third case occurs when the clipped parts of the image are not white but have a color. In such cases, we restore the missing image details in the clipped color channels by analyzing the scene information available in other color channels in the captured image. For each method, we obtain a maximum-a-posteriori estimate of the unknown HDR image by analyzing and inverting the forward imaging process.

Text

2018-06-25

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/66350

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/