Compressed sensing applied in ultraviolet compressive imaging

Compressed Sensing (CS) is a highly anticipated signal processing technique in various fields, which can break through the limitations of Nyquist sampling theorem and recover the original signal with fewer samples. This article focuses on the application of CS in UV compression imaging and proposes a prototype system for UV compression imaging based on CS. The system utilizes a spatial light modulator (SLM) and utilizes the CS sparsity principle to reduce the amount of sampled data, thereby lowering the system's sampling cost and data transmission bandwidth requirements. Through numerical simulation experiments, a digital optical device resolution board was used as the object, and a random Gaussian matrix was used to simulate the SLM modulation process. The TVAL3 algorithm was used to reconstruct high-resolution objects from low dimensional measurement data, and the imaging quality was evaluated using indicators such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean square error (MSE). The experimental results show that CS technology can effectively reconstruct signals at low sampling rates, verifying its feasibility in the ultraviolet band and providing a new approach for practical ultraviolet imaging and spectral analysis. However, this study was only validated through simulation experiments and has not been fully validated in real physical environments. It can be further expanded towards dynamic range modulation and multimodal fusion in the future.