Exploring Sub-Nyquist Sampling for Fine Image Reconstruction

Compared to traditional methods, compressive sensing, a signal processing technique, allows for the effective acquisition and reconstruction of signals. A rate that is less than twice the highest frequency component of a signal is known as subNyquist sampling, allows for the digitization of wideband frequency-domain sparse signals without compromising reconstruction quality when employing compression sensing (CS). According to the CS framework, samples taken at sub- Nyquist sampling rates can be used to reconstruct signals with sparse representations in a known basis. Because of its inherent qualities, the Fourier domain is frequently used in compressive sensing applications. Use of a small number of Fourier transform coefficients in sparse signal recovery applications has led to successful resolutions of large-scale data recovery issues, including image recovery. During the measurement phase, input is systematically measured in blocks to obtain the necessary data, while in the recovery phase, all measurements from a single image are simultaneously utilized to reconstruct the complete image.