In this study, a linear and phase-based Eulerian video magnification (EVM) methods are developed to minimize magnified noises and processing time. The developed approaches utilize the Lanczos resampling algorithm to reduce the frames’ size of the source video so that the size of the processed data is significantly reduced. Then spatial decomposition is applied to the resized frames. Subsequently, temporal filters with specific cut-off frequencies are also used to filter only the desired frequencies to be amplified and then add them to the decomposed frames. The magnified frames are processed by a wavelet denoising algorithm to locate distributed noise over the different frequency bands and then remove it. The resulted denoised-magnified frames are resized up and then reconstructed by the spatial synthesis process. The experiments show the superiority and effectiveness of the developed EVM approaches compared to the conventional ones and other related approaches in terms of the execution time and the quality of the magnified video. The developed EVM approach can be used in several applications such as the detection of human vital signs without contact so that it is very useful to avoid infection in several diseases such as Covid-19. Furthermore, it can be used in detection of human mood and lying detection, detection and localization of material and liquid variations.