Multi-frame super-resolution algorithm using common vector approach

• Published in: IET image processing 2018-12, Vol.12 (12), p.2292-2299
• Main Authors: Seke, Erol, Anagün, Yıldıray, Adar, Nihat
• Format: Article
• Language: English
• Subjects: common vector approach; consecutive frames; difference vectors; high‐resolution image; HR image blocks; HR image reconstruction; image denoising; image reconstruction; image resolution; image sequences; low‐resolution image; motion estimation; multiframe super‐resolution algorithm; multiple LR images; multiple sources; noise reduction method; peak signal‐to‐noise ratio; Research Article; shadows; super‐resolution applications; translational optical flow; vectors; video sequence; visual signal‐to‐noise ratio
• ISSN: 1751-9659; 1751-9667; 1751-9667
• DOI: 10.1049/iet-ipr.2018.5168

Super-resolution (SR) applications aim to use information within one or more low-resolution (LR) image(s) to obtain high-resolution (HR) image(s). LR images might well be the consecutive frames of a video sequence. When multiple LR images are used as input, motion estimation (ME) between portions of images is an important step of the solution for this ill-posed problem. In this study, the authors employed translational optical flow for ME, followed by common vector approach (CVA) for HR image reconstruction from multiple sources. CVA provides a way to reduce outliers caused by noise, occlusion, shadows and incorrect ME. HR image blocks are obtained by combining common and difference vectors of the blocks’ class that are handled separately. Noise in difference vectors is reduced by a known noise reduction method before combining. Separate handling of common and difference parts guarantees better results and greatly reduce artefacts. Compared to the state-of-the-art, experimental results confirm the authors’ achievement by visual, peak signal-to-noise ratio and structural similarity index measures criteria.