Cameras have long grappled with the challenge of capturing the vast range of light intensities present in the real world and reproducing them on a medium with much lower dynamic range. This challenge persisted from the era of print photography and continued with the adoption of standard dynamic range (SDR) displays, such as CRT and LCD displays. However, the emergence of high dynamic range (HDR) displays, such as OLED and dual-modulation LCD-LED, has enabled more accurate reproduction of a wider range of light intensities.

To fully leverage the capabilities of these HDR displays, it is essential to capture images that faithfully represent the range of light intensities in the real world. In this dissertation, I propose improvements to the HDR capture and reconstruction pipeline by developing noise-aware statistical estimation methods that use physically accurate parametric models to approximate the noise distribution of pixels. These methods address inaccuracies in existing merging algorithms and do not rely on camera-specific parameters that can be difficult to calibrate accurately. Accurate HDR reconstruction also requires accounting for small inaccuracies in the reported camera metadata caused by changes in lighting or rounding by the camera firmware.

The proposed HDR reconstruction pipeline has the potential to enhance existing HDR datasets. To demonstrate its effectiveness, I evaluate the improved pipeline using two datasets designed for different applications. The reconstruction pipeline also plays a crucial role in an ultra-realistic capture-display-render system built to faithfully reproduce our 3D world. The psychophysical experiments conducted on this prototype display, which showed that the reconstructed 3D scenes are indistinguishable from their real counterparts, serve as the most comprehensive validation of the HDR pipeline, as they focus on reconstructing real-world scenes in a physically accurate manner.

Additionally, I investigate the effectiveness of learning-based methods on the challenging task of predicting HDR information from a single 8-bit photograph. This problem requires the hallucination of underexposed and overexposed pixels, making it more difficult than merging an exposure stack. Through a controlled subjective experiment, I found that state-of-the-art deep convolutional networks are only partially successful at reconstructing accurate HDR pixels. I highlight the limitations of existing evaluation protocols, which explain the poor correlations between widely-used HDR image quality metrics and subjective data. Building upon these findings, I propose a simple correction that significantly improves the predictions of quality metrics for this task.

One observation from the study is that the single-image HDR problem is treated as a one-to-one mapping by most existing works. In response to this, I present an alternative approach that utilises a generative network to model the one-to-many mapping for global operations. This approach offers several advantages, including the ability to generate multiple plausible HDR images from a single input photograph, thereby enhancing the accuracy of HDR reconstruction.

By tackling the challenges associated with capturing and reconstructing HDR information from still scenes, as well as predicting HDR information from single images, this research has the potential to make significant advancements in the field of HDR imaging. Through these advancements, we can take a significant step forward in bridging the gap between the dynamic ranges observed in the real world and those present in our digital content.