Reconstruction from periodic nonlinearities, with applications to HDR imaging

Shah, Viraj; Soltani, Mohammadreza; Hegde, Chinmay

doi:10.1109/acssc.2017.8335471

Cited by 8 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When working with wrapped intensities of natural images, instead of optical phase values, the complex interplay of high spatial frequencies and drastically varying light intensity has to be accounted for. Unwrapping techniques for natural images has been analyzed [7] and tailored algorithms developed [27,55,56], but these require multiple input images. Most recently, the UnModNet network architecture was introduced to unwrap a single intensity image with state-ofthe-art quality [67].…”

Section: Related Workmentioning

confidence: 99%

MantissaCam: Learning Snapshot High-dynamic-range Imaging with Perceptually-based In-pixel Irradiance Encoding

So¹,

Martel²,

Dudek³

et al. 2021

Preprint

View full text Add to dashboard Cite

The ability to image high-dynamic-range (HDR) scenes is crucial in many computer vision applications. The dynamic range of conventional sensors, however, is fundamentally limited by their well capacity, resulting in saturation of bright scene parts. To overcome this limitation, emerging sensors offer in-pixel processing capabilities to encode the incident irradiance. Among the most promising encoding schemes is modulo wrapping, which results in a computational photography problem where the HDR scene is computed by an irradiance unwrapping algorithm from the wrapped low-dynamic-range (LDR) sensor image. Here, we design a neural network-based algorithm that outperforms previous irradiance unwrapping methods and, more importantly, we design a perceptually inspired "mantissa" encoding scheme that more efficiently wraps an HDR scene into an LDR sensor. Combined with our reconstruction framework, MantissaCam achieves state-of-the-art results among modulo-type snapshot HDR imaging approaches. We demonstrate the efficacy of our method in simulation and show preliminary results of a prototype MantissaCam implemented with a programmable sensor.

show abstract

Section: Related Workmentioning

confidence: 99%

MantissaCam: Learning Snapshot High-dynamic-range Imaging with Perceptually-based In-pixel Irradiance Encoding

So¹,

Martel²,

Dudek³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the multi-shot approach depends on carefully designed camera exposures, while our approach succeeds for non-designed (generic) linear observations; moreover, they do not include sparsity in their model reconstructions. In our previous work [4], we proposed a different extension based on [3,26] for signal recovery from quantized modulo measurements, which can also be adapted for sparse measurements, but there too the measurements need to be carefully designed.…”

Section: Modulo Recoverymentioning

confidence: 99%

“…This specific form of signal recovery is gaining rapid interest in recent times. Recently, the use of a novel imaging sensor that wraps the data in a periodical manner has been shown to overcome certain hardware limitations of typical imaging systems [2][3][4][5]. Many image acquisition systems suffer from the problem of limited dynamic range; however, real-world signals can contain a large range of intensity levels, and if tuned incorrectly, most intensity levels can lie in the saturation region of the sensors, causing loss of information through signal clipping.…”

Section: Introductionmentioning

confidence: 99%

Sparse Signal Recovery from Modulo Observations

Shah

Hegde

2020

Preprint

Self Cite

View full text Add to dashboard Cite

We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.

show abstract

“…However, the multi-shot approach depends on carefully designed camera exposures, while our approach succeeds for nondesigned (generic) linear observations; moreover, they do not include sparsity in their model reconstructions. In our previous work [3], we proposed a different extension based on [2], [31] for signal recovery from quantized modulo measurements, which can also be adapted for sparse measurements, but there too the measurements need to be carefully designed.…”

Section: Prior Workmentioning

confidence: 99%

Signal Reconstruction From Modulo Observations

Shah

Hegde

2019

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Self Cite

View full text Add to dashboard Cite

We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less wellknown imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the underdetermined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.

show abstract

Reconstruction from periodic nonlinearities, with applications to HDR imaging

Cited by 8 publications

References 17 publications

MantissaCam: Learning Snapshot High-dynamic-range Imaging with Perceptually-based In-pixel Irradiance Encoding

MantissaCam: Learning Snapshot High-dynamic-range Imaging with Perceptually-based In-pixel Irradiance Encoding

Sparse Signal Recovery from Modulo Observations

Signal Reconstruction From Modulo Observations

Contact Info

Product

Resources

About