Super-resolution lensless imaging system based on a fast anti-diffraction algorithm

Article Optics

Design and single-shot fabrication of lensless cameras with arbitrary point spread functions

Kyung Chul Lee et al.

Summary: Lensless cameras are a new type of computational imaging devices that use a thin mask instead of lenses to achieve compact and low-cost hardware. This paper proposes a method for fast fabrication of lensless cameras with arbitrary point spread functions (PSFs). The method includes designing smooth phase mask profiles for a given PSF pattern and fabricating the mask using gray-scale lithography technique. Compared to existing approaches, this method allows for ultra-fast and cost-effective fabrication of phase masks, making it suitable for mass production and commercialization of lensless cameras. The method can be used to produce custom lensless cameras with various pre-designed PSFs and obtain images of the scene through computational image reconstruction. The paper also discusses the future directions and potential applications of custom lensless cameras, including fast imaging and fingerprint detection.

OPTICA (2023)

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Article Optics

Wave-optics-based image synthesis for super resolution reconstruction of a FZA lensless camera

Xiao Chen et al.

Summary: This research focuses on the theoretical analysis of the wave-optics imaging model of an FZA lensless camera and proposes a novel image synthesis method to compensate for the zero points caused by diffraction. The proposed methods show nearly two-fold improvement in spatial resolution compared to the conventional geometrical-optics based method, as verified through computer simulations and optical experiments.

OPTICS EXPRESS (2023)

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Article Optics

Explicit-restriction convolutional framework for lensless imaging

Yuchen Ma et al.

Summary: Mask-based lensless cameras offer flexible imaging systems by breaking the constraints of traditional lens-based cameras. However, the limitations of imaging devices result in low reconstruction quality. To address this challenge, we propose an explicit-restriction convolutional framework that effectively incorporates multiple constraints by introducing linear and nonlinear terms. By customizing the framework, better image quality or higher pixel density can be achieved.

OPTICS EXPRESS (2022)

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Article Optics

Image reconstruction with transformer for mask-based lensless imaging

Xiuxi Pan et al.

Summary: A mask-based lensless camera encodes and reconstructs images optically to improve image reconstruction. Conventional model-based approaches are affected by imperfect system modeling, while pure data-driven deep neural networks have the potential to provide better reconstruction quality. However, existing pure DNN reconstruction methods for lensless imaging do not outperform model-based methods.

OPTICS LETTERS (2022)

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Article Optics

DNN-FZA camera: a deep learning approach toward broadband FZA lensless imaging

Jiachen Wu et al.

Summary: This study presents a prototype of a lensless camera using a deep neural network for rapid reconstruction in Fresnel zone aperture imaging. By connecting a deep back-projection network behind a U-Net, an error feedback mechanism is provided for self-correction of image details, with training data generated using a diffraction model.

OPTICS LETTERS (2021)

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Article Optics

Incoherent reconstruction-free object recognition with mask-based lensless optics and the Transformer

Xiuxi Pan et al.

Summary: A mask-based lensless camera saves on weight and cost compared to a lensed camera, but requires additional computation for image reconstruction. By performing object recognition directly on the encoded pattern, computational resources are saved and errors in reconstruction avoided. This approach also allows for better extraction of global features.

OPTICS EXPRESS (2021)

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Article Computer Science, Artificial Intelligence