As the title says, this would be a parameter that allows the quality measurement parameters to be computed against a known image, rather than change per iteration.
Python based dashboard for real-time Electrical Impedance Tomography including image reconstruction using Back Projection, Graz Consensus and Gauss Newton methods
My implementaion of super-resolution based on dual-path networks. It can be used directly to reconstruct your low-resolution image to high-resolution. Also, you can use your dataset to train your networks
OPTICS (Ordering Points To Identify the Clustering Structure) is a clustering algorithm similar to DBSCAN. DBSCAN's major weakness is density tuning. OPTICS attempts to address this issue by ordering points and choosing the best epsilon.
We currently have an incomplete OPTICS implementation at [utils/clust
As the title says, this would be a parameter that allows the quality measurement parameters to be computed against a known image, rather than change per iteration.
Something like:
[img, qual] = SIRT(proj,geo,angles, 'QualMeas','RMSE','ground_truth',my_image)