Super-Resolution Image Reconstruction Using Dual-Mode Complex Diffusion-Based Shock Filter and Singular Value Decomposition

Abstract

Super-resolution (SR) algorithms are widely used to overcome the hardware limitations of the low-cost image acquisition devices. In this paper, we present a single image SR (SISR) approach in wavelet domain, which simultaneously preserves the contrast and edge information. Our algorithm uses the notion of geometric duality to generate the initial estimation of unknown high-resolution (HR) image, by applying covariance-based interpolation. State-of-the-art wavelet techniques for SISR provide resolution enhancement by replacing the low-frequency subband with the input low-resolution image. This leads to non-uniform illumination in the super-resolved image. The proposed method exploits singular value decomposition to correct the low-frequency subband, as obtained via stationary wavelet transform (SWT). The modified low-frequency subband and the high-frequency subband images are subjected to Lanczos interpolation. The interpolated subbands are filtered by employing diffusion-based shock filter, which operates in the dual dominant modes. All the filtered subband images are fused to generate the final HR image, by applying inverse SWT. Our experimental analysis has demonstrated the superiority of the proposed method in preserving the edges with uniform illumination.