Spatio-Temporal Super-Resolution for CS-Based ToF 3D Imaging

Paredes, Alvaro Lopez; Conde, Miguel Heredia; Ibrahim, Thoriq; Pham, Anh Ngoc; Kagawa, Keiichiro

doi:10.23919/eusipco58844.2023.10289980

Cited by 2 publications

References 22 publications

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A Macro-Pixel Based RGBZ CMOS Image Sensor for Simultaneous Color and Multifrequency Depth Image Acquisition

Shahandashti,

López,

Brea

et al. 2024

2024 International Workshop on the Theory of Computational Sensing and Its Applications to Radar, Multimodal Sensing and Imagin

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This paper introduces a novel system architecture designed for on-chip simultaneous color (RGB) and multifrequency depth (Z) measurement utilizing an indirect Time of Flight (iToF) image sensor. The proposed macro-pixel structure enables the acquisition of multi-frame data in a single shot, albeit at the cost of trading off the frame rate for the resolution. The electrical simulations demonstrate the robust performance of the system, showcasing its capability to achieve single-shot reconstruction of RGBZ images. The proposed architecture yields RGB images relatively comparable in quality to those obtained from conventional RGBZ sensors while enhancing the accuracy of depth images compared to prevailing 2D/3D reconstruction methodologies because of the multifrequency approach.

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A Macro-Pixel Based RGBZ CMOS Image Sensor for Simultaneous Color and Multifrequency Depth Image Acquisition

Shahandashti,

López,

Brea

et al. 2024

2024 International Workshop on the Theory of Computational Sensing and Its Applications to Radar, Multimodal Sensing and Imagin

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Super-Resolution Image Reconstruction of Wavefront Coding Imaging System Based on Deep Learning Network

Li,

Yu,

et al. 2024

Electronics

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Wavefront Coding (WFC) is an innovative technique aimed at extending the depth of focus (DOF) of optics imaging systems. In digital imaging systems, super-resolution digital reconstruction close to the diffraction limit of optical systems has always been a hot research topic. With the design of a point spread function (PSF) generated by a suitably phase mask, WFC could also be used in super-resolution image reconstruction. In this paper, we use a deep learning network combined with WFC as a general framework for images reconstruction, and verify its possibility and effectiveness. Considering the blur and additive noise simultaneously, we proposed three super-resolution image reconstruction procedures utilizing convolutional neural networks (CNN) based on mean square error (MSE) loss, conditional Generative Adversarial Networks (CGAN), and Swin Transformer Networks (SwinIR) based on mean absolute error (MAE) loss. We verified their effectiveness by simulation experiments. A comparison of experimental results shows that the SwinIR deep residual network structure based on MAE loss optimization criteria can generate more realistic super-resolution images with more details. In addition, we used a WFC camera to obtain a resolution test target and real scene images for experiments. Using the resolution test target, we demonstrated that the spatial resolution could be improved from 55.6 lp/mm to 124 lp/mm by the proposed super-resolution reconstruction procedure. The reconstruction results show that the proposed deep learning network model is superior to the traditional method in reconstructing high-frequency details and effectively suppressing noise, with the resolution approaching the diffraction limit.

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Spatio-Temporal Super-Resolution for CS-Based ToF 3D Imaging

Cited by 2 publications

References 22 publications

A Macro-Pixel Based RGBZ CMOS Image Sensor for Simultaneous Color and Multifrequency Depth Image Acquisition

A Macro-Pixel Based RGBZ CMOS Image Sensor for Simultaneous Color and Multifrequency Depth Image Acquisition

Super-Resolution Image Reconstruction of Wavefront Coding Imaging System Based on Deep Learning Network

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