Mr. Lintao Peng Profile

Lintao Peng

at Beijing Institute of Technology

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Proceedings Article | 4 January 2023 Presentation + Paper

Large-scale single-pixel imaging via deep learning

Siyu Xie, Lintao Peng, Liheng Bian

Proceedings Volume 12317, 1231703 (2023) https://doi.org/10.1117/12.2643014

KEYWORDS: Computer programming, Transformers, Image restoration, Image quality, Feature extraction, Sensors, Image resolution, Reconstruction algorithms, Modulation, Convolution

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Single-pixel imaging uses a single-pixel detector to capture all photons emitted from the two-dimensional scene, and then calculates and reconstructs the two-dimensional target scene image from the one-dimensional measurement data through single-pixel reconstruction methods (such as linear superposition, compressed sensing or deep learning) based on the one-dimensional acquisition data and the corresponding illumination coding. Compared with traditional cameras, single-pixel imaging has the advantages of high signal-to-noise ratio and wide spectrum. Due to these advantages, single-pixel imaging has been widely used in multispectral imaging. However, the traditional single-pixel image reconstruction methods have some disadvantages, such as low resolution, huge time consuming and poor reconstruction quality. In this paper, we propose a single-pixel image reconstruction method based on neural network. Compared with the traditional single-pixel image reconstruction method, this method has better reconstruction quality at lower sampling rate. Specifically, in this model, we first use a small optimized-patterns to simulate a single-pixel camera to sample the image to obtain the measured values, and then extract multi-channel high-dimensional semantic features from the sampled values through a high-dimensional semantic feature extraction network. Then, the multi-scale residual network module is used to construct the feature pyramid up-sampling module to up sample the high-dimensional semantic features. In the training process, the network parameters and pattern are jointly optimized to obtain the optimal network model and pattern. With the help of large-scale and pre-training, our reconstructed image has higher resolution, shorter reconstruction time and better reconstruction quality.

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