✦ LIBER ✦

Image Super-Resolution by TV-Regularization and Bregman Iteration

✍ Scribed by Antonio Marquina; Stanley J. Osher

Publisher: Springer US
Year: 2008
Tongue: English
Weight: 564 KB
Volume: 37
Category: Article
ISSN: 0885-7474
DOI: 10.1007/s10915-008-9214-8

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Blind deconvolution using TV regularizat

Blind deconvolution using TV regularization and Bregman iteration

✍ Lin He; Antonio Marquina; Stanley J. Osher 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 397 KB

## Abstract In this paper we formulate a new time dependent model for blind deconvolution based on a constrained variational model that uses the sum of the total variation norms of the signal and the kernel as a regularizing functional. We incorporate mass conservation and the nonnegativity of the

Generating dense and super-resolution IS

Generating dense and super-resolution ISAR image by combining bandwidth extrapolation and compressive sensing

✍ YingHui Quan; Lei Zhang; Rui Guo; MengDao Xing; Zheng Bao 📂 Article 📅 2011 🏛 Science in China Press (SCP) 🌐 English ⚖ 574 KB

Super-resolution of the undersampled and

Super-resolution of the undersampled and subpixel shifted image sequence by a neural network

✍ Yao Lu; Minoru Inamura; Maria del Carmen Valdes 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 245 KB

## Abstract Numerous approaches to super‐resolution (SR) of sequentially observed images (image sequence) of low resolution (LR) have been presented in the past two decades. However, neural network methods are almost ignored for solving SR problems. This is because the SR problem traditionally has