Multi-wavelet level comparison on compressive sensing for MRI image reconstruction - Indrarini Dyah Irawati; Sugondo Hadiyoso; Yuli Sun Hariyani (Institute of Advanced Engineering and Science, 2020)
In this study, weproposed compressive sampling for MRI reconstruction based on sparse representation using multi-wavelet transformation. Comparing the performance of wavelet decomposition level, which are level 1, level 2, level 3, and level 4. We used gaussian random process to generate measurement matrix. The algorithm used to reconstruct the image is l_1 norm. The experimental results showed that the use of wavelet multi-level can generate higher compression ratio but requires a longer processing time. MRI reconstruction results based on the parameters of the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) show that the higher the level of decomposition in wavelets, the value of both decreases.