An edge preserving IBP based super resolution image reconstruction using P-spline and MuCSO-QPSO algorithm

Super resolution (SR) reconstruction based on iterative back projection (IBP) is a widely used image reconstruction method. IBP approach is easy to implement and allows easy inclusion of the spatial domain with low computational complexity. However, local minima trapping; slow rate of convergence; sensitive to the initial guess; prone to ringing and jaggy artifacts are some major bottlenecks which restrict its performance. The present paper aims to enhance the performance of IBP based SR reconstruction (IBP-SRR) of image by exploring an effective method. The proposed method has fast convergence rate, a global optimal solution, capability to lessen the effect of artifacts and a noble generalization performance. In the present work, P-spline interpolation scheme imposes additional penalty in the inherently smooth B-spline interpolation process to provide a proper initial guess. An adaptive edge regularization technique is used in the constraint optimization of the reconstruction problem to minimize the effect of ringing artifacts. Finally, the overall reconstruction error of the reconstruction system is optimized using a hybrid meta-heuristic optimization technique. The optimization algorithm hybridizes the notion of Cuckoo search optimization (CSO) algorithm with a mutation operator (MuCSO) and the quantum behaved particle swarm optimization (QPSO). The MuCSO-QPSO algorithm is compared with other significant optimization algorithms such as GA, PSO, QPSO, CSO, MuCSO and found to be outperforming. Experimental results demonstrate the superiority of the proposed edge preserving IBP-SRR method in terms of enhanced spatial resolution, and more detail reconstruction.