Sparse representation based multi-frame image super-resolution reconstruction using adaptive weighted features

• Published in: IET image processing 2019-03, Vol.13 (4), p.663-672
• Main Authors: Nandi, Debashis, Karmakar, Jayashree, Kumar, Amish, Mandal, Mrinal Kanti
• Format: Article
• Language: English
• Subjects: adaptive weighted feature operators; adaptive weighted features; feature extraction; high‐resolution frame; image reconstruction; image registration; image representation; image resolution; low‐resolution frames; LR frames; multiframe image super‐resolution reconstruction; noisy input frames; reconstructed image; Research Article; single image SR technique; sparse representation; sub‐pixel accuracy; sub‐pixel level
• ISSN: 1751-9659; 1751-9667; 1751-9667
• DOI: 10.1049/iet-ipr.2018.5139

This study presents a novel sparse-representation based multi-frame super-resolution (SR) technique to reconstruct a high-resolution (HR) frame from multiple noisy low-resolution (LR) frames by using registration in sub-pixel accuracy and adaptive weighted feature operators. First, the registration of multiple frames in sub-pixel level and the mapping of pixels from LR frames to HR grid puts more information into the reconstructed image with respect to the conventional sparse representation based single image SR technique. This improves the overall resolution of the output image. Second, the introduction of adaptive weighted feature operators in the reconstruction process has significantly improved the robustness of the algorithm to noisy input frames. Hence, from the outputs, it can be seen that the proposed method outperforms the recent techniques in terms of noise robustness even in the higher noise level in the input image. The performance of the proposed algorithm is evaluated and quantified through a set of well-defined quality metrics and compared with some recently developed techniques. The results of the proposed technique confirm the claims of the authors.