Although High Dynamic Range (HDR) imaging has been the subject of significant research over the past fifteen years, the goal of acquiring cinema-quality HDR images of fast-moving scenes using available components has not yet been achieved. In this work, we present an optical architecture for HDR imaging that allows simultaneous capture of high, medium, and low-exposure images on three sensors at high fidelity with efficient use of the available light. We also present an HDR merging algorithm to complement this architecture, which avoids undesired artifacts when there is a large exposure difference between the images. We implemented a prototype high-definition HDR-video system and we present still frames from the acquired HDR video, tonemapped with various techniques.

A versatile HDR video production system

http://people.csail.mit.edu/pdidyk/papers/ComputationalDisplaysSurvey.pdf

Special Section on Advanced Displays: A survey on computational displays: Pushing the boundaries of optics, computation, and perception

Managing the appearance of images across different display environments is a difficult problem, exacerbated by the proliferation of high dynamic range imaging technologies. Tone reproduction is often limited to luminance adjustment and is rarely calibrated against psychophysical data, while color appearance modeling addresses color reproduction in a calibrated manner, albeit over a limited luminance range. Only a few image appearance models bridge the gap, borrowing ideas from both areas. Our take on scene reproduction reduces computational complexity with respect to the state-of-the-art, and adds a spatially varying model of lightness perception. The predictive capabilities of the model are validated against all psychophysical data known to us, and visual comparisons show accurate and robust reproduction for challenging high dynamic range scenes.

Calibrated image appearance reproduction

One of the most successful approaches to modern high quality HDR-video capture is to use camera setups with multiple sensors imaging the scene through a common optical system. However, such systems pose several challenges for HDR reconstruction algorithms. Previous reconstruction techniques have considered debayering, denoising, resampling (align- ment) and exposure fusion as separate problems. In contrast, in this paper we present a unifying approach, performing HDR assembly directly from raw sensor data. Our framework includes a camera noise model adapted to HDR video and an algorithm for spatially adaptive HDR reconstruction based on fitting of local polynomial approximations to observed sensor data. The method is easy to implement and allows reconstruction to an arbitrary resolution and output mapping. We present an implementation in CUDA and show real-time performance for an experimental 4 Mpixel multi-sensor HDR video system. We further show that our algorithm has clear advantages over existing methods, both in terms of flexibility and reconstruction quality.

A Unified Framework for Multi-Sensor HDR Video Reconstruction

Assessment of video tone-mapping: Are cameras' S-shaped tone-curves good enough?

The main goal of High Dynamic Range Imaging (HDRI) is precise reproduction of real world appearance in terms of intensity levels and color gamut at all stages of image and video processing from acquisition to display. In our work, we investigate the problem of lossy HDR image and video compression and provide a number of novel solutions, which are optimized for storage efficiency or backward compatibility with existing compression standards. To take advantage of HDR information even for traditional low-dynamic range displays, we design tone mapping algorithms, which adjust HDR contrast ranges in a scene to those available in typical display devices.

Selected Problems of High Dynamic Range Video Compression and GPU-based Contrast Domain Tone Mapping

The main goal of High Dynamic Range Imaging (HDRI) is precise reproduction of real world appearance in terms of intensity levels and color gamut at all stages of image and video processing from acquisition to display In our work, we investigate the problem of lossy HDR image and video compression and provide a number of novel solutions, which are optimized for storage efficiency or backward compatibility with existing compression standards To take advantage of HDR information even for traditional low-dynamic range displays, we design tone mapping algorithms, which adjust HDR contrast ranges in a scene to those available in typical display devices

Dorota Zdrojewska

Papers

Selected Problems of High Dynamic Range Video Compression and GPU-based Contrast Domain Tone Mapping

Selected problems of high dynamic range video compression and GPU-based contrast domain tone mapping