Compressive Sensing (CS) has emerged as a transformative technique in image compression, offering innovative solutions to challenges in efficient signal representation and acquisition.
1. Introduction
In the era of rapid technological advancement, the sheer volume of data generated and exchanged daily has become staggering. This influx of information, from high-resolution images to bandwidth-intensive videos, has posed unprecedented challenges to conventional methods of data transmission and storage. As we grapple with the ever-growing demand for the efficient handling of these vast datasets, a groundbreaking concept emerges—Compressive Sensing
[1].
Traditionally, the Nyquist–Shannon sampling theorem
[2] has governed our approach to capturing and reconstructing signals, emphasizing the need to sample at twice the rate of the signal’s bandwidth to avoid information loss. However, in the face of escalating data sizes and complexities, this theorem’s practicality is increasingly strained. Compressive Sensing, as a disruptive force, challenges the assumptions of Nyquist–Shannon by advocating for a selective and strategic sampling technique.
In general, CS is a revolutionary signal-processing technique that hinges on the idea that sparse signals can be accurately reconstructed from a significantly reduced set of measurements. The principle of CS involves capturing a compressed version of a signal, enabling efficient data acquisition and transmission. As the
Figure 1 shows, in the field of image processing, numerous works have been proposed in each of various domains, exploring innovative techniques and algorithms to harness the potential of compressive imaging
[3,4[3][4][5][6][7][8][9][10][11],
5,6,7,8,9,10,11], efficient communication systems
[12[12][13][14][15][16][17][18][19][20],
13,14,15,16,17,18,19,20], pattern recognition
[21,22[21][22][23][24][25][26][27][28][29],
23,24,25,26,27,28,29], and video processing tasks
[30,31,32,33,34,35,36,37,38][30][31][32][33][34][35][36][37][38]. The versatility and effectiveness of CS make it a compelling area of study with broad implications across different fields of signal processing and information retrieval.