Enhanced Iterative Back-Projection Based Super-Resolution Reconstruction of Digital Images

Abstract

Iterative back-projection (IBP) is a popular and straightforward approach applied successfully in the field of image super-resolution reconstruction (SRR). SRR using IBP (SRR–IBP) methods efficiently satisfies the basic reconstruction constraints. In spatial domain applications, it allows easy inclusion of data and is a computationally efficient method. However, inferior convergence rate, sensitivity to the initial choice of image, the presence of different degrees of ringing artifacts are some of the major disadvantages that limit the performance of SRR–IBP. To relieve these inherent limitations, an evolutionary edge preserving IBP (EEIBP) is proposed in this paper. The proposed work introduces an improved initial choice of the digital image by interpolating the low-resolution digital image via hybridizing the notion of uniform and non-uniform B-spline interpolation. Secondly, it incorporates a spatially adaptive back-projecting kernel (SABPK) and regularization constraints in the iterative process. The SABPK utilizes covariance-based adaptation to restore the lost high-frequency details and is regulated by a control parameter to make the reconstruction process robust. The regularization constraints use different low-level feature descriptors to track the information related to shape and salient visual properties of the digital image. Finally, the overall reconstruction error is minimized via GA, PSO and cuckoo search (CS) algorithms. Experimental results demonstrate the robustness and the effectiveness of the proposed EEIBP method to provide a high-resolution solution with improved visual perception and reduced artifacts. Moreover, EEIBP method optimized via CS algorithm enables a better quality of reconstruction as compared the other search algorithms (gradient, GA and PSO).