Programmable spatially variant single-pixel imaging based on compressive sensing

Abstract

Single-pixel camera is developed to mitigate the constraints faced by the conventional cameras especially in invisible wavelengths and low light conditions. Nyquist–Shannon theorem requires as many measurements as the image pixels to reconstruct images flawlessly. In practice, obtaining more measurements increases the cost and acquisition time, which are the major drawbacks of single-pixel imaging (SPI). Therefore, compressive sensing was proposed to enable image reconstruction with fewer measurements. We present a design of sensing patterns to obtain image information by utilizing spatially variant resolution (SVR) technique in SPI. The proposed method reduces the measurements by prioritizing the resolution in the region of interest (ROI). It successfully achieves the programmable imaging concept where multiresolution adaptively optimizes the balance between the image quality and the measurements number. Results show that SVR images can be reconstructed from significantly fewer measurements yet able to achieve better image quality than uniform resolution images. In addition, the SVR images can be further enhanced by integrating the dynamic supersampling technique. Consequently, the concerns of image quality, long acquisition, and processing time can be addressed. The proposed method potentially benefits imaging applications where the target ROI is prioritized over the background and most importantly it requires fewer measurements.