Accelerated framework for image compression and reconstruction based on compressive sensing

Abstract

Image compression is a crucial field driven by advancements in communication and imaging technologies. Its primary goal is to achieve low bit rates while maintaining high-quality image reconstruction. Compression is essential in digital image processing, multimedia applications, and medical imaging. Various algorithms exist for image compression and reconstruction, each differing in efficiency. Compressive sensing (CS) algorithms, commonly used for radar data reconstruction, require iterative computations that demand significant processing power and time, limiting real-time applications. To overcome these challenges, this study proposes a parallel-pipelined processing approach to enhance compression and reconstruction efficiency. The method accelerates processing speeds, increases data throughput, and optimizes performance by reducing data size. The proposed approach divides image data into multiple parallel processing branches, significantly reducing computational cycles. This results in faster execution and improved real-time applicability. MATLAB simulations and field-programmable gate array (FPGA) hardware implementations have been conducted to validate the system’s effectiveness. The results demonstrate that the parallel-pipelined method significantly enhances efficiency compared to traditional approaches, making it suitable for applications requiring high-speed image processing, such as satellite imaging and medical diagnostics.