Open AccessSparse representation based multi‐frame image super‐resolution reconstruction using adaptive weighted features
Author(s) -
Nandi Debashis,
Karmakar Jayashree,
Kumar Amish,
Mandal Mrinal Kanti
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5139
Subject(s) - robustness (evolution) , artificial intelligence , pixel , computer science , sparse approximation , computer vision , pattern recognition (psychology) , image resolution , iterative reconstruction , frame (networking) , feature (linguistics) , noise (video) , image (mathematics) , telecommunications , biochemistry , chemistry , linguistics , philosophy , gene
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.