Deconvolution and Signal Restoration as Advanced Signal Processing Techniques

In signal processing technology, deconvolution and signal restoration constitute a theoretically challenging subfield. Given their broad applicability, they have consistently remained focal points of research. While related studies are dispersed across numerous specialized academic publications, students and researchers entering the field require a systematic guide reflecting recent developments and providing structured knowledge. This publication addresses that need.

The authors have systematically organized fundamental knowledge required in this domain. Beyond covering the physical origins of deconvolution problems, key theoretical methodologies, applicable ranges, and limitations, the book includes practical programming examples with implementation details (such as Wiener filter algorithms, Richardson-Lucy deconvolution methods, and regularization techniques) and real-world datasets. These elements significantly assist readers in transitioning to practical applications. Simultaneously, the text delivers in-depth theoretical foundations and frontier developments, catering to readers pursuing advanced research.

In summary, through detailed explanations, profound analyses, and abundant examples including code snippets demonstrating critical functions like Fourier transform operations and point spread function modeling, this book provides a comprehensive guide to deconvolution and signal restoration techniques. It empowers readers to better understand and apply domain knowledge effectively.