Contrast and Spatial Resolution Practice Test

Edge-preservation deconvolution relies on balancing fidelity to the observed data with a penalty that preserves edges while reducing noise. When this balance isn’t applied correctly, the solution can become unstable and start to misinterpret noise as structure or create artificial edges and ringing around real edges. That happens when the regularization is not properly configured—either the weight is inappropriate or the type of penalty doesn’t match the problem—so the method overfits or under-regularizes the data. In contrast, using too much regularization tends to smooth out details and edges rather than create edge artifacts, and too weak regularization is a form of improper setup that still falls under not properly regularized. Data capture issues like a detector with very high dynamic range don’t directly cause edge-preservation artifacts in the deconvolution process, since the problem is primarily about how the image is regularized during reconstruction.