Advancements in Signal Processing: A Comprehensive Review of Discrete Wavelet Transform and Fractional Wavelet Filter Techniques

This review paper explores the fundamental importance of fractional wavelet filter (FrWF) and discrete wavelet transform (DWT) techniques in signal processing. These techniques are thoroughly examined and serve as the foundation for many different applications, including data compression, telecommunications, and medical diagnostics. DWT’s multi-resolution analysis, facilitated by breaking signals into various scales, plays a vital role in image compression, denoising, and feature extraction. FrWF, designed for memory-constrained environments, notably reduces memory overhead through external memory utilization. The adaptive memory allocation technique enhances memory efficiency, pivotal for real-time applications like sensors and cameras. These techniques, distinct yet impactful, bolster science and technology. This review highlights their vital roles, progressions, and potential in reshaping memory-restricted signal processing, redefining paradigms.