An Algorithm Architecture Co-Design for CMOS Compressive High Dynamic Range Imaging

Guicquero, William; Dupret, Antoine

doi:10.1109/tci.2016.2557073

Cited by 17 publications

(4 citation statements)

References 36 publications

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“…Several approaches encode information into the captured LDR image to allow for better estimation of highlights. This can be achieved by modifying the sensor architecture through spatially varying pixel exposures [48,18], convolutional sparse coding [58], compressed sensing [16], or modulo cameras [68]. Drawbacks of these approaches include the need for expensive custom cameras and loss of detail in the low dynamic range.…”

Section: Related Workmentioning

confidence: 99%

Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging

Sun

Tseng

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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High-dynamic range (HDR) imaging is an essential imaging modality for a wide range of applications in uncontrolled environments, including autonomous driving, robotics, and mobile phone cameras. However, existing HDR techniques in commodity devices struggle with dynamic scenes due to multi-shot acquisition and postprocessing time, e.g. mobile phone burst photography, making such approaches unsuitable for real-time applications. In this work, we propose a method for snapshot HDR imaging by learning an optical HDR encoding in a single image which maps saturated highlights into neighboring unsaturated areas using a diffractive optical element (DOE). We propose a novel rank-1 parameterization of the DOE which drastically reduces the optical search space while allowing us to efficiently encode high-frequency detail. We propose a reconstruction network tailored to this rank-1 parametrization for the recovery of clipped information from the encoded measurements. The proposed end-to-end framework is validated through simulation and real-world experiments and improves the PSNR by more than 7 dB over state-ofthe-art end-to-end designs.

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Section: Related Workmentioning

confidence: 99%

Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging

Sun

Tseng

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Compressive sensing (CS) based HDR imaging was proposed by Guicquero et al [53]. Their method utilizes cellular automaton for a scalable and low-complexity column based compressive sensing.…”

Section: Application Specific Integrated Circuitsmentioning

confidence: 99%

“…Also, most of the embedded systems would have limited system resources in terms of battery, computational power and memory resources. These design challenges require novel solutions, and in this report we focus on these issues.In this we survey will discuss those tonemap algorithms which have been implemented on GPU [1-10], FPGA , and ASIC [42][43][44][45][46][47][48][49][50][51][52][53] in terms of their hardware specifications and performance. Output image quality is an important metric for tonemap algorithms.…”

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confidence: 99%

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Real-time Tone Mapping: A State of the Art Report

Ou,

Ambalathankandy,

Ikebe

et al. 2020

Preprint

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The rising demand for high quality display has ensued active research in high dynamic range (HDR) imaging, which has the potential to replace the standard dynamic range imaging. This is due to HDR's features like accurate reproducibility of a scene with its entire spectrum of visible lighting and color depth. But this capability comes with expensive capture, display, storage and distribution resource requirements. Also, display of HDR images/video content on an ordinary display device with limited dynamic range requires some form of adaptation. Many adaptation algorithms, widely known as tone mapping operators, have been studied and proposed in the last few decades. In this state of the art report, we present a comprehensive survey of 50+ tone mapping algorithms that have been implemented on hardware for acceleration and real-time performance. These algorithms have been adapted or redesigned to make them hardware-friendly. This effort leads to various design challenges that are encountered during the hardware development. Any real-time application poses strict timing constraints which requires time exact processing of the algorithm. Also, most of the embedded systems would have limited system resources in terms of battery, computational power and memory resources. These design challenges require novel solutions, and in this report we focus on these issues.In this we survey will discuss those tonemap algorithms which have been implemented on GPU [1-10], FPGA , and ASIC [42][43][44][45][46][47][48][49][50][51][52][53] in terms of their hardware specifications and performance. Output image quality is an important metric for tonemap algorithms. From our literature survey we found that, various objective quality metrics have been used to demonstrate the functionality of adapting the algorithm on hardware platform. We have compiled and studied all the metrics used in this survey [54][55][56][57][58][59][60][61][62][63][64][65][66][67]. Finally, in this report we demonstrate the link between hardware cost and image quality thereby illustrating the underlying trade-off. This report concludes with a discussion on the general future research directions based-on various hardware design/implementation bottlenecks which will be useful for the research community.

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Single‐Shot Auto‐Exposure and High Dynamic Range Imaging Based on Asynchronous Operation of Pixel Array Circuitry

Lamouaraa‐Sedlackova,

Fernández‐Berni,

Carmona‐Galán

2024

Circuit Theory & Apps

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This paper introduces an image formation technique that realizes automatic adaptation to the illumination conditions during image capture. This adaptation takes place asynchronously in a single shot, requiring neither external control nor iterations on the exposure setting. These characteristics are especially suitable for applications such as visual robot navigation, where illumination may abruptly change and immediate decisions must be made. The equations that model the proposed technique show exposure‐time independence. Moreover, each pixel adapts itself according to the ratio between local and global illuminations. The dependence on this ratio—which is a relative rather than absolute magnitude—along with the aforementioned independence on exposure time means that, theoretically, any illumination scenario can be represented within an arbitrary signal range. We also address the CMOS implementation of this technique. An extensive set of electrical simulations confirms its auto‐exposure and high‐dynamic‐range encoding capabilities. In particular, we simulated the capture of a 118‐dB scene on a ‐px array over five orders of magnitude of ambient illumination. Corner and mismatch simulations reveal strong robustness of the proposed circuitry against fabrication non‐idealities. Finally, although we aim to applications in which large image resolution is not a critical specification, strategies to minimize the impact of the required focal‐plane circuit elements on the pixel pitch are examined.

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An Algorithm Architecture Co-Design for CMOS Compressive High Dynamic Range Imaging

Cited by 17 publications

References 36 publications

Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging

Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging

Real-time Tone Mapping: A State of the Art Report

Single‐Shot Auto‐Exposure and High Dynamic Range Imaging Based on Asynchronous Operation of Pixel Array Circuitry

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