Iterative blob-based super-resolution reconstruction with wavelet denoising

It has been shown previously that incorporating blob-based basis functions into super-resolution reconstruction can guarantee better results and save computational time, and we are able to use a lower number of low-resolution datasets for the super-resolution reconstruction. Although blob-based basis functions are effective on suppressing image noise during the reconstruction compare with the ordinary pixel-based reconstruction, this is only limited to image reconstruction from low-resolution datasets which are not excessively corrupted with random noise. For raw image datasets with excessive noise, we can use wavelets to perform pre-processing noise reduction before the datasets are used for the superresolution reconstruction. Wavelet denoising can effectively separate image noise from useful image features which is sometimes necessary for pre-processing before image reconstruction.