Motion Magnification Videos sources and results

This dataset investigates the suitability of different filters for Learning-Based Motion Magnification (LBMM) and examines the impact of filter parameters on output results. The study finds that the Butterworth filter produces satisfactory results, while the analysis of IIR filters is unsatisfactory due to computational and memory limitations. Additionally, the efficacy of IIR filters for image processing and the reliability of FIR filters are called into question. The study observes that the number of the filter order used in a Butterworth filter has a considerable effect on output results, with filter order 3 exhibiting no adverse effects. Furthermore, the study highlights the importance of adhering to the Nyquist theorem when recording physical phenomena for image processing to ensure accurate and high-quality results. The dataset concludes that filter selection and parameter adjustment should be thoughtfully considered to achieve better results in this specific use case.