Lensless light-field imaging with Fresnel zone aperture: quasi-coherent coding

Shimano, Takeshi; Nakamura, Yusuke; Tajima, Kazuyuki; Sao, Mayu; Hoshizawa, Taku

doi:10.1364/ao.57.002841

Cited by 74 publications

(38 citation statements)

References 8 publications

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“…In a lensless imaging system, the scene is captured either directly on the sensor [10] or after being modulated by a mask element. Types of masks that have been used include phase gratings [11], random diffusers [4], designed phasemasks [2], amplitude masks [1], [12], compressive samplers [13], [14] and spatial light modulators [15], [16]. Replacing lens with the above masks result in multiplexed sensor capture that lacks any resemblance to the scene imaged.…”

Section: Lensless Imagingmentioning

confidence: 99%

FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements

Khan

Sundar

Boominathan

et al. 2020

IEEE Trans. Pattern Anal. Mach. Intell.

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Lensless imaging has emerged as a potential solution towards realizing ultra-miniature cameras by eschewing the bulky lens in a traditional camera. Without a focusing lens, the lensless cameras rely on computational algorithms to recover the scenes from multiplexed measurements. However, the current iterative-optimization-based reconstruction algorithms produce noisier and perceptually poorer images. In this work, we propose a non-iterative deep learning-based reconstruction approach that results in orders of magnitude improvement in image quality for lensless reconstructions. Our approach, called FlatNet, lays down a framework for reconstructing high-quality photorealistic images from mask-based lensless cameras, where the camera's forward model formulation is known. FlatNet consists of two stages: (1) an inversion stage that maps the measurement into a space of intermediate reconstruction by learning parameters within the forward model formulation, and (2) a perceptual enhancement stage that improves the perceptual quality of this intermediate reconstruction. These stages are trained together in an end-to-end manner. We show high-quality reconstructions by performing extensive experiments on real and challenging scenes using two different types of lensless prototypes: one which uses a separable forward model and another, which uses a more general non-separable cropped-convolution model. Our end-to-end approach is fast, produces photorealistic reconstructions, and is easy to adopt for other mask-based lensless cameras.

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Section: Lensless Imagingmentioning

confidence: 99%

FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements

Khan

Sundar

Boominathan

et al. 2020

IEEE Trans. Pattern Anal. Mach. Intell.

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show abstract

“…We assumed the artifacts were caused by the discontinuity of the captured coded image at the border of the pixel area in the image sensor. We also considered that the vignetting of the reconstructed image was caused by the design of the chief-ray angle (CRA) characteristics of each pixel in the consumer image sensor used, as mentioned also in [6]. This can be solved by controlling the CRA characteristics.…”

Section: Experiments Using a Mask-based Lensless Camera And Simulatedmentioning

confidence: 99%

“…In a lensless camera, a lens system is typically replaced with a coded aperture such as a coding mask, which makes the inverse problem numerically invertible. As the coded aperture, amplitude masks [4][5][6] or phase-modulation optics [7][8][9][10] have been installed in front of the image sensor. In computational lensless imaging, spatial information of a subject is coded by the coded aperture, and the optically coded image is sampled by the image sensor.…”

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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“…Inspired by this, Mertz and Young proposed zone plate coded imaging 25 and extended the concept and application of holography to the field of incoherent light. Recent works have demonstrated the imaging capability of the Fresnel zone plate, also called the Fresnel zone aperture (FZA), at visible wavelengths [26][27][28][29] . This prototype camera needs at least four FZAs with different phases to extract the signal from captured images.…”

Section: Introductionmentioning

confidence: 99%

Single-shot lensless imaging with fresnel zone aperture and incoherent illumination

Zhang

et al. 2020

Light Sci Appl

119

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Lensless imaging eliminates the need for geometric isomorphism between a scene and an image while allowing the construction of compact, lightweight imaging systems. However, a challenging inverse problem remains due to the low reconstructed signal-to-noise ratio. Current implementations require multiple masks or multiple shots to denoise the reconstruction. We propose single-shot lensless imaging with a Fresnel zone aperture and incoherent illumination. By using the Fresnel zone aperture to encode the incoherent rays in wavefront-like form, the captured pattern has the same form as the inline hologram. Since conventional backpropagation reconstruction is troubled by the twin-image problem, we show that the compressive sensing algorithm is effective in removing this twin-image artifact due to the sparsity in natural scenes. The reconstruction with a significantly improved signal-to-noise ratio from a single-shot image promotes a camera architecture that is flat and reliable in its structure and free of the need for strict calibration.

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Lensless light-field imaging with Fresnel zone aperture: quasi-coherent coding

Cited by 74 publications

References 8 publications

FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements

FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements

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

Single-shot lensless imaging with fresnel zone aperture and incoherent illumination

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