Single exposure high dynamic range imaging with a conventional camera using cross-screen filters

Real world scenes often contain both bright and dark regions, resulting in a high contrast ratio, beyond the capabilities of conventional cameras. For these cases, High Dynamic Range or HDR images can be captured with expensive hardware or by taking multiple exposures of the same scene. However, these methods cost extra resources -- either spatial or temporal resolution is sacrificed, or more than one piece of hardware is needed. In this thesis, a novel technique is presented that is capable of capturing High Dynamic Range images in only one exposure of a conventional camera. We observe that most natural HDR images have only 2-5% pixels that are too bright compared to the rest of the scene to fall inside the dynamic range of a conventional camera. Our method spreads energy from these bright regions into the neighboring unsaturated pixels by defocus blurring. Bright pixels still get clipped in the captured image due to saturation of the sensor; but some information about these clipped pixels gets encoded or multiplexed in the form of superimposed glare patterns in the image. Frequency preservation and decoding of this information can be further improved by using a cross-screen filter instead of using defocus blur. Superimposed glare patterns are recovered with the help of natural image statistics. These glare patterns provide information about how much energy there is in the saturated pixels, which allows a tomography-like reconstruction of the saturated regions. Once the saturated regions are known, the rest of the image can be restored by removing the estimated glare patterns.Science, Faculty ofComputer Science, Department ofGraduat

Computational single-image high dynamic range imaging

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.Science, Faculty ofComputer Science, Department ofGraduat

Gradient Domain Color Restoration of Clipped Highlights

Sensor clipping destroys the hue of colored highlight regions by misrepresenting the relative magnitude of the color channels. This becomes particularly noticeable in regions with brightly colored light sources or specularities. We present a simple yet effective gradient-space color restoration algorithm for recovering the hue in such image regions. First, we estimate a smooth distribution of the hue of the affected region from information at its boundary. We combine this hue estimate with gradient information from channels unaffected by clipping to restore clipped color channels. 1

Stochastic Deconvolution

We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks. Unlike previous methods that must be tailored to specific regularization strategies, the new Stochastic Deconvolution method allows arbitrary priors, including nonconvex and data-dependent regularizers, to be introduced and tested with little effort. Stochastic Deconvolution is straightforward to implement, produces state-of-the-art results and directly leads to a natural boundary condition for image boundaries and saturated pixels. 1