Computational ghost imaging using deep learning

Shimobaba, Tomoyoshi; Endo, Yutaka; Nishitsuji, Takashi; Takahashi, Takayuki; Nagahama, Yuki; Hasegawa, Satoki; Sano, M.; Hirayama, Ryuji; Kakue, Takashi; Shiraki, Atsushi; Ito, Tomoyoshi

doi:10.1016/j.optcom.2017.12.041

Cited by 134 publications

(47 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An application of CNNs developed by many groups has been to make reconstructions of an image from random patterns [79][80][81]. This computational GI using a CNN has produced equivalent results to a regulariser method (as discussed in section 7).…”

Section: Machine Learningmentioning

confidence: 99%

Single-pixel imaging 12 years on: a review

2020

View full text Add to dashboard Cite

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.

show abstract

Section: Machine Learningmentioning

confidence: 99%

Single-pixel imaging 12 years on: a review

2020

View full text Add to dashboard Cite

show abstract

“…In recent years, deep learning receives much attention in many research fields including optical design [1,2] and optical imaging [3]. In previous works, deep learning has been extensively applied for many optical imaging problems including phase retrieval [4][5][6][7], microscopic image enhancement [8][9], scattering imaging [10][11], holography [12][13][14][15][16][17][18], single-pixel imaging [19,20], super-resolution [21][22][23][24], Fourier ptychography [25][26][27], optical interferometry [28,29], wavefront sensing [30,31], and optical fiber communications [32].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…An iterative algorithm is generally used for the reconstruction, but can be computationally intensive. Recently, deep learning has been used to solve the inverse problem in several computational imaging modalities, replacing iterative methods [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], including in Fourier ptychographic microscopy [39].…”

Section: Introductionmentioning

confidence: 99%

Optimal physical preprocessing for example-based super-resolution

Robey¹,

Ganapati²

2018

Opt. Express

View full text Add to dashboard Cite

In example-based super-resolution, the function relating low-resolution images to their high-resolution counterparts is learned from a given dataset. This data-driven approach to solving the inverse problem of increasing image resolution has been implemented with deep learning algorithms. In this work, we explore modifying the imaging hardware in order to collect more informative low-resolution images for better ultimate high-resolution image reconstruction. We show that this "physical preprocessing" allows for improved image reconstruction with deep learning in Fourier ptychographic microscopy.Fourier ptychographic microscopy is a technique allowing for both high resolution and high field-of-view at the cost of temporal resolution. In Fourier ptychographic microscopy, variable illumination patterns are used to collect multiple low-resolution images. These low-resolution images are then computationally combined to create an image with resolution exceeding that of any single image from the microscope. We use deep learning to jointly optimize the illumination pattern with the post-processing reconstruction algorithm for a given sample type, allowing for single-shot imaging with both high resolution and high field-of-view. We demonstrate, with simulated data, that the joint optimization yields improved image reconstruction as compared with sole optimization of the post-processing reconstruction algorithm.

show abstract

Computational ghost imaging using deep learning

Cited by 134 publications

References 18 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?

Optimal physical preprocessing for example-based super-resolution

Contact Info

Product

Resources

About