Single-Pixel Hyperspectral Imaging via an Untrained Convolutional Neural Network

Abstract

Single-pixel hyperspectral imaging (HSI) has received a lot of attention in recent years due to its advantages of high sensitivity, wide spectral ranges, low cost, and small sizes. In this article, we perform a single-pixel HSI experiment based on an untrained convolutional neural network (CNN) at an ultralow sampling rate, where the high-quality retrieved images of the target objects can be achieved by every visible wavelength of a light source from 432 nm to 680 nm. Specifically, we integrate the imaging physical model of single-pixel HSI into a randomly initialized CNN, which allows the images to be reconstructed by relying solely on the interaction between the imaging physical process and the neural network without pre-training the neural network.

Single-Pixel Infrared Hyperspectral Imaging via Physics-Guided Generative Adversarial Networks

“…The details of the DGI-based single-pixel hyperspectral imaging can be found in Ref. [26], on the basis of which we will introduce our physics-driven GAN framework for its reconstruction in the following.…”

“…However, the need for high-resolution images in HSI to capture fine scene details inevitably leads to substantial data acquisition, increased sampling time (rate), and higher processing and storage costs. The introduction and fusion of compressed sensing (CS) algorithms and SPI provide one of the promising alternative solutions for HSI [26][27][28][29][30][31][32][33], effectively addressing the challenges associated with HSI by utilizing undersampling and single-pixel detection. An illustrative study demonstrates the effectiveness of a CS-based single-pixel HSI method for detecting the chemical composition of targets in the nearinfrared spectrum.…”

“…It highlights the method's potential for efficient and cost-effective chemical composition detection through high-compression spectral analysis [34]. The other approach is to introduce deep neural networks on the foundation of CS-SPI and other techniques [26][27][28][31][32][33][35][36][37][38][39][40]. A physics-driven convolutional neural network (CNN) was integrated with a single-pixel HSI model to achieve high-quality image reconstruction across a wide range of visible wavelengths at an ultralow sampling rate [26], inspired by the first GI scheme using a physics-driven deep CNN constraint (e.g., GIDC) [41].…”

“…The other approach is to introduce deep neural networks on the foundation of CS-SPI and other techniques [26][27][28][31][32][33][35][36][37][38][39][40]. A physics-driven convolutional neural network (CNN) was integrated with a single-pixel HSI model to achieve high-quality image reconstruction across a wide range of visible wavelengths at an ultralow sampling rate [26], inspired by the first GI scheme using a physics-driven deep CNN constraint (e.g., GIDC) [41]. Then, a residual degradation learning unfolding network was provided to be applied in compressive spectral imaging [42], and a blind unsupervised neural network was designed to deal with hyperspectral and RGB images, all achieving high-level HSI reconstruction [43].…”

Resolution-enhanced single-pixel imaging using the Hadamard transform matrix

“…Over more than a decade, there have been many reports about the ordering of these basis patterns in SPI. [16][17][18][53][54][55] Besides various CS and CS-based SPI schemes, [24,25] SPI via deep learning [26][27][28][29][30] has been also proposed to not only improve the quality of reconstructed images but greatly reduce the sampling number.…”