Image Quality Enhancement for Single-Image Super Resolution Based on Local Similarities and Support Vector Regression

スポンサーリンク

概要

• 論文の詳細を見る

• In reconstruction-based super resolution, a high-resolution image is estimated using multiple low-resolution images with sub-pixel misalignments. Therefore, when only one low-resolution image is available, it is generally difficult to obtain a favorable image. This letter proposes a method for overcoming this difficulty for single- image super resolution. In our method, after interpolating pixel values at sub-pixel locations on a patch-by-patch basis by support vector regression, in which learning samples are collected within the given image based on local similarities, we solve the regularized reconstruction problem with a sufficient number of constraints. Evaluation experiments were performed for artificial and natural images, and the obtained high-resolution images indicate the high-frequency components favorably along with improved PSNRs.

• 2011-02-01

著者

• Hamamoto Takayuki

  Graduate School of Engineering Tokyo University of Science

• YAGUCHI Atsushi

  Graduate School of Engineering, Tokyo University of Science

• HOSAKA Tadaaki

  Faculty of Engineering Division I, Tokyo University of Science

• Yaguchi Atsushi

  Graduate School Of Engineering Tokyo University Of Science

• Hosaka Tadaaki

  Faculty Of Engineering Division I Tokyo University Of Science

関連論文

• FREE VIEWPOINT IMAGE RECONSTRUCTION FROM 3-D MULTI-FOCUS IMAGING SEQUENCES AND ITS IMPLEMENTATION BY CELL-BASED COMPUTING(International Workshop on Advanced Image Technology 2009)
• Image Quality Enhancement for Single-Image Super Resolution Based on Local Similarities and Support Vector Regression
• Sensor-Pattern-Noise Map Reconstruction in Source Camera Identification for Size-Reduced Images

スポンサーリンク