Lensless Imaging: A computational renaissance

Boominathan, Vivek; Adams, Jesse K.; Asif, M. Salman; Avants, Benjamin W.; Robinson, Jacob T.; Baraniuk, Richard G.; Sankaranarayanan, Aswin C.; Veeraraghavan, Ashok

doi:10.1109/msp.2016.2581921

Cited by 117 publications

(42 citation statements)

References 30 publications

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“…(2) An alternating gradient descent-based refinement algorithm that jointly estimates the light distribution and depth map on a continuous domain. cameras avoid this problem by increasing the number of pinholes and allowing more light to reach the sensor [4], [8]- [11]. In contrast to a pinhole camera where only one inverted image of the scene is obtained through a single pinhole, the measurements captured through a coded-mask are a linear combination of all the pinhole images under every mask element.…”

Section: Related Workmentioning

confidence: 99%

“…The 3D lensless imaging problem has also recently been studied in [5], [11], [13], [14]. These methods can broadly be divided into two categories.…”

Section: Related Workmentioning

confidence: 99%

“…In the second category, the 3D scene is divided into an intensity map and multiple depth planes such that each pixel is assigned one intensity and depth. To solve the intensity and depth recovery problem, these methods either sweep through the depth planes [11] or assign depth to each pixel using a greedy method [5]. Our proposed method belongs to the second category in which we model the image intensity and depth separately and assume that the depth values of the scene are distributed on a continuous domain.…”

Section: Related Workmentioning

confidence: 99%

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Image and Depth Estimation with Mask-Based Lensless Cameras

Zheng

Asif

2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Mask-based lensless cameras replace the lens of a conventional camera with a customized mask. These cameras can potentially be very thin and even flexible. Recently, it has been demonstrated that such mask-based cameras can recover light intensity and depth information of a scene. Existing depth recovery algorithms either assume that the scene consists of a small number of depth planes or solve a sparse recovery problem over a large 3D volume. Both these approaches fail to recover scene with large depth variations. In this paper, we propose a new approach for depth estimation based on alternating gradient descent algorithm that jointly estimates a continuous depth map and light distribution of the unknown scene from its lensless measurements. The computational complexity of the algorithm scales linearly with the spatial dimension of the imaging system. We present simulation results on image and depth reconstruction for a variety of 3D test scenes. A comparison between the proposed algorithm and other method shows that our algorithm is faster and more robust for natural scenes with a large range of depths.

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Section: Related Workmentioning

confidence: 99%

“…The 3D lensless imaging problem has also recently been studied in [5], [11], [13], [14]. These methods can broadly be divided into two categories.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Image and Depth Estimation with Mask-Based Lensless Cameras

Zheng

Asif

2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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“…Unlike lens-based imaging systems where the goal is to project a reproduction of a (potentially magnified) scene onto an image sensor, a lensless imaging system seeks to produce an invertible transfer function between the incident light field and the sensor measurements. These measurements often may not resemble a traditional image [24][25][26][27][28][29][30] but contain sufficient information for a computational algorithm to reconstruct an image. The major advantage for lensless imaging of fluorescence is the substantial benefits in FOV, threedimensional capture, light-collection efficiency and form-factor [22][23][24]31 .…”

Section: Introductionmentioning

confidence: 99%

In vivofluorescence imaging with a flat, lensless microscope

Adams

Boominathan

Gao

et al. 2020

Preprint

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Fluorescence imaging over large areas of the brain in freely behaving animals would allow researchers to better understand the relationship between brain activity and behavior; however, traditional microscopes capable of high spatial resolution and large fields of view (FOVs) require large and heavy lenses that restrict animal movement. While lensless imaging has the potential to achieve both high spatial resolution and large FOV with a thin lightweight device, lensless imaging has yet to be achieved in vivo due to two principal challenges: (a) biological tissue typically has lower contrast than resolution targets, and (b) illumination and filtering must be integrated into this non-traditional device architecture. Here, we show that in vivo fluorescence imaging is possible with a thin lensless microscope by optimizing the phase mask and computational reconstruction algorithms, and integrating fiber optic illumination and thin-film color filters. The result is a flat, lensless imager that achieves better than 10 μm spatial resolution and a FOV that is 30× larger than other cellular resolution miniature microscopes.

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“…A computational lensless camera [1] works on the basis of computational imaging, which is a combination of optical encoding and computational decoding [2], instead of lens-based optical imaging. This frees the camera from the need for optical focusing, allowing the camera to be implemented with ultra-thin, miniature hardware [3].…”

Section: Introductionmentioning

confidence: 99%

Super Field-of-View Lensless Camera by Coded Image Sensors

Nakamura

Kagawa

Torashima

et al. 2019

Preprint

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A lensless camera is an ultra-thin computational-imaging system. Existing lensless cameras are based on the axial arrangement of an image sensor and a coding mask, and therefore, the back side of the image sensor cannot be captured. In this paper, we propose a lensless camera with a novel design that can capture the front and back sides simultaneously. The proposed camera is composed of multiple coded image sensors, which are complementary-metal-oxide-semiconductor~(CMOS) image sensors in which air holes are randomly made at some pixels by drilling processing. When the sensors are placed facing each other, the object-side sensor works as a coding mask and the other works as a sparsified image sensor. The captured image is a sparse coded image, which can be decoded computationally by using compressive-sensing-based image reconstruction. We verified the feasibility of the proposed lensless camera by simulations and experiments. The proposed thin lensless camera realizes super field-of-view imaging without lenses or coding masks, and therefore can be used for rich information sensing in confined spaces. This work also suggests a new direction in the design of CMOS image sensors in the era of computational imaging.

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Lensless Imaging: A computational renaissance

Cited by 117 publications

References 30 publications

Image and Depth Estimation with Mask-Based Lensless Cameras

Image and Depth Estimation with Mask-Based Lensless Cameras

In vivofluorescence imaging with a flat, lensless microscope

Super Field-of-View Lensless Camera by Coded Image Sensors

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