Efficient Spatially-Variant Single-Pixel Imaging Using Block-Based Compressed Sensing

Single-pixel imaging is an important alternative to conventional camera. Only a single-pixel detector is needed to capture image data by measuring the correlation of the target scene and a series of sensing patterns. Conventionally, Nyquist-Shannon theorem requires measurements not less than the image pixels for an error-free reconstruction. Compressed sensing (CS) enables image reconstructions with fewer measurements but the image quality and computational cost remain the primary concerns. This paper presents an efficient single-pixel imaging technique based on blocked-based CS in which the sensing matrices are designed based on spatially-variant resolution (SVR). The proposed method decreases the number of measurements as well as the image reconstruction time using the SVR sensing patterns. Furthermore, it takes advantage of block-based CS to reduce the expenses of computational resources. The proposed method is evaluated and compared to conventional uniform resolution (UR) image reconstruction in terms of image quality and reconstruction time. The results show that the proposed method consistently reduces the reconstruction time and able to give better image quality at lower sampling ratio (SR). This provides an efficient reconstruction for single-pixel imaging which is desirable in practical application and situations where low sampling rate is required.