I'm currently a sophomore in college and pretty new to the field of research. I'm currently working on an existing algorithm for MRI denoising and the results are nothing that great. I can't see how I can improve the results. I've tried a few different tweaks and played with the code and parameters for sometime now. I also saw some papers on using neural networks for image denoising as well. I'm thinking of trying the same but I don't know if it makes sense to use neural networks on an already existing algorithm. I mean won't the neural network overpower my algorithm and all the results be based on it only. So if I use a pre-existing Matlab filter and use neural networks in it, will it differ much if I first use my algorithm and then use neural networks in it. I have little experience with machine learning and hope that I made sense in my question. Any kind of help will be appreciated. Thanks.
My personal feeling is that you should do each things separately and compare the results. For example, take your MRI dataset and denoise using "standard algorithm 1", "standard algorithm 2" and "neural network algorithm 1". I would keep things reasonably simple unless you have a good justification of doing "standard algorithm 2 and neural network algorithm 1". Doing two in a row is going to be harder to justify in a paper.
(I think the other question that is difficult is how to tell if one denoising algorithm is "better" than another. I have written a couple papers on standard image processing denoising in MRI and it is always a difficult question to answer. Also, if the output of the denoising is a dataset that is to be interpreted by a radiologist then other consideration would have to happen vs denoising as a pre-step to some scientific quantification.)
One thing you could do is look through papers, for example at https://arxiv.org/ to see if there are other neural network denoising examples that can help guide you in parameter selection.