Signal to Noise Ratio in Lensless Compressive Imaging
This work addresses noise performance in compressive imaging systems, which is incremental as it builds on existing LCI and compressive sensing frameworks.
The paper analyzes the signal-to-noise ratio (SNR) in lensless compressive imaging (LCI) and compares it to pinhole or lens-based imaging, finding that LCI's SNR remains constant regardless of image resolution, whereas it decreases in traditional methods as resolution increases, leading to higher SNR for LCI at large resolutions.
We analyze the signal to noise ratio (SNR) in a lensless compressive imaging (LCI) architecture. The architecture consists of a sensor of a single detecting element and an aperture assembly of an array of programmable elements. LCI can be used in conjunction with compressive sensing to capture images in a compressed form of compressive measurements. In this paper, we perform SNR analysis of the LCI and compare it with imaging with a pinhole or a lens. We will show that the SNR in the LCI is independent of the image resolution, while the SNR in either pinhole aperture imaging or lens aperture imaging decreases as the image resolution increases. Consequently, the SNR in the LCI is much higher if the image resolution is large enough.