Enhancing super-resolution reconstructed image quality in 3D MR images using simulated annealing
Super-resolution reconstruction (SRR) algorithms are used for getting high-resolution (HR) data from low-resolution observations. In Maximum a posteriori (MAP) based SRR the observation model is employed for estimating a HR image that best reproduces the two low-resolution input data sets. The parameters of the prior play a significant role in the MAP based SRR. This work concentrates on the investigation of the influence of one such parameter, called temperature, on the reconstructed 3D MR images. The existing approaches on SRR in 3D MR images use a constant value for this parameter. We use a cooling schedule similar to simulated annealing for computing the value of the temperature parameter at each iteration of the SRR. We have used 3D MR cardiac data sets in our experiments and have shown that the iterative computation of the temperature which resembles simulated annealing delivers better results.