A picture is worth a billion bits: Real-time image reconstruction from dense binary threshold pixels
Remez T, Litany O, Bronstein AM. 2016. A picture is worth a billion bits: Real-time image reconstruction from dense binary threshold pixels. 2016 IEEE International Conference on Computational Photography (ICCP). IEEE International Conference on Computational Photography, 7492874.
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https://doi.org/10.48550/arXiv.1510.04601
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Conference Paper
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Scopus indexed
Author
Remez, Tal;
Litany, Or;
Bronstein, Alex M.ISTA
Abstract
The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels (jots), which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artefacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator. Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor.
Publishing Year
Date Published
2016-06-16
Proceedings Title
2016 IEEE International Conference on Computational Photography (ICCP)
Publisher
IEEE
Article Number
7492874
Conference
IEEE International Conference on Computational Photography
Conference Location
Evanston, IL, United States
Conference Date
2016-05-13 – 2016-05-15
IST-REx-ID
Cite this
Remez T, Litany O, Bronstein AM. A picture is worth a billion bits: Real-time image reconstruction from dense binary threshold pixels. In: 2016 IEEE International Conference on Computational Photography (ICCP). IEEE; 2016. doi:10.1109/iccphot.2016.7492874
Remez, T., Litany, O., & Bronstein, A. M. (2016). A picture is worth a billion bits: Real-time image reconstruction from dense binary threshold pixels. In 2016 IEEE International Conference on Computational Photography (ICCP). Evanston, IL, United States: IEEE. https://doi.org/10.1109/iccphot.2016.7492874
Remez, Tal, Or Litany, and Alex M. Bronstein. “A Picture Is Worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Threshold Pixels.” In 2016 IEEE International Conference on Computational Photography (ICCP). IEEE, 2016. https://doi.org/10.1109/iccphot.2016.7492874.
T. Remez, O. Litany, and A. M. Bronstein, “A picture is worth a billion bits: Real-time image reconstruction from dense binary threshold pixels,” in 2016 IEEE International Conference on Computational Photography (ICCP), Evanston, IL, United States, 2016.
Remez T, Litany O, Bronstein AM. 2016. A picture is worth a billion bits: Real-time image reconstruction from dense binary threshold pixels. 2016 IEEE International Conference on Computational Photography (ICCP). IEEE International Conference on Computational Photography, 7492874.
Remez, Tal, et al. “A Picture Is Worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Threshold Pixels.” 2016 IEEE International Conference on Computational Photography (ICCP), 7492874, IEEE, 2016, doi:10.1109/iccphot.2016.7492874.
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arXiv 1510.04601