Multi-frame super-resolution algorithm using common vector approach<?show [AQ ID=Q1]?>

SEKE E., ANAGÜN Y., ADAR N.

IET IMAGE PROCESSING, cilt.12, sa.12, ss.2292-2299, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 12
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1049/iet-ipr.2018.5168
  • Dergi Adı: IET IMAGE PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2292-2299
  • Anahtar Kelimeler: motion estimation, image reconstruction, image resolution, image sequences, vectors, image denoising, HR image blocks, difference vectors, visual signal-to-noise ratio, peak signal-to-noise ratio, multiframe super-resolution algorithm, common vector approach, super-resolution applications, low-resolution image, high-resolution image, consecutive frames, video sequence, multiple LR images, motion estimation, translational optical flow, HR image reconstruction, multiple sources, shadows, noise reduction method, IMAGE-RECONSTRUCTION, MOTION, RESOLUTION
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

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.