Optical machine learning with incoherent light and a single-pixel detector

Jiao, Shuming; Feng, Jun; Gao, Yang; Lei, Ting; Xie, Zhenwei; Yuan, Xiaocong

doi:10.1364/ol.44.005186

Cited by 105 publications

(30 citation statements)

References 19 publications

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“…Finally, using optical machine learning with a single-pixel detector may also be a possibility for image reconstruction. A diffractive neural network made up of a cascade of phase-only masks can reconstruct images without requiring the processing power of a computer [83].…”

Section: Machine Learningmentioning

confidence: 99%

Single-pixel imaging 12 years on: a review

2020

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Modern cameras typically use an array of millions of detector pixels to capture images. By contrast, single-pixel cameras use a sequence of mask patterns to filter the scene along with the corresponding measurements of the transmitted intensity which is recorded using a single-pixel detector. This review considers the development of single-pixel cameras from the seminal work of Duarte et al. up to the present state of the art. We cover the variety of hardware configurations, design of mask patterns and the associated reconstruction algorithms, many of which relate to the field of compressed sensing and, more recently, machine learning. Overall, single-pixel cameras lend themselves to imaging at non-visible wavelengths and with precise timing or depth resolution. We discuss the suitability of single-pixel cameras for different application areas, including infrared imaging and 3D situation awareness for autonomous vehicles.

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Section: Machine Learningmentioning

confidence: 99%

Single-pixel imaging 12 years on: a review

2020

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“…The random-phase-encoded optical cryptosystem is usually coherent while the single-pixel imaging is usually incoherent. In the previous work [37], it is shown that a multiple-phasemask diffractive system and a single-pixel imaging system are similar from several aspects such as performing optical pattern recognition. In the previous works [34,35], each system is modeled by a different deep learning network optimized with many pairs of input and output training samples.…”

Section: Introductionmentioning

confidence: 99%

Does deep learning always outperform simple linear regression in optical imaging?

et al. 2020

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Deep learning has been extensively applied in many optical imaging problems in recent years. Despite the success, the limitations and drawbacks of deep learning in optical imaging have been seldom investigated. In this work, we show that conventional linear-regression-based methods can outperform the previously proposed deep learning approaches for two black-box optical imaging problems in some extent. Deep learning demonstrates its weakness especially when the number of training samples is small. The advantages and disadvantages of linear-regression-based methods and deep learning are analyzed and compared. Since many optical systems are essentially linear, a deep learning network containing many nonlinearity functions sometimes may not be the most suitable option.

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“…Different from the coherent imaging system [11,12], an incoherent optical system such as a single-pixel imaging (SPI) system [16][17][18][19][20][21] will have significantly lower experimental complexity. In fact, this has been revealed in some previous works about optical computing [22] and holography [23]. The object image is usually captured by a pixelated sensor array in a conventional optical imaging system.…”

Section: Introductionmentioning

confidence: 97%

Visual cryptography in single-pixel imaging

Jiao¹,

Feng²,

Gao³

et al. 2020

Opt. Express

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107

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Two novel visual cryptography (VC) schemes are proposed by combining VC with single-pixel imaging (SPI) for the first time. It is pointed out that the overlapping of visual key images in VC is similar to the superposition of pixel intensities by a single-pixel detector in SPI. In the first scheme, QR-code VC is designed by using opaque sheets instead of transparent sheets. The secret image can be recovered when identical illumination patterns are projected onto multiple visual key images and a single detector is used to record the total light intensities. In the second scheme, the secret image is shared by multiple illumination pattern sequences and it can be recovered when the visual key patterns are projected onto identical items. The application of VC can be extended to more diversified scenarios by our proposed schemes.

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Optical machine learning with incoherent light and a single-pixel detector

Cited by 105 publications

References 19 publications

Single-pixel imaging 12 years on: a review

Single-pixel imaging 12 years on: a review

Does deep learning always outperform simple linear regression in optical imaging?

Visual cryptography in single-pixel imaging

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