A Nonlinear L1-Norm Approach for Joint Image Registration and Super-Resolution

Kim Hui Yap; Yu He; Yushuang Tian; Lap Pui Chau

doi:10.1109/LSP.2009.2028106

A Nonlinear L₁-Norm Approach for Joint Image Registration and Super-Resolution

Kim Hui Yap
, Yu He
, Yushuang Tian
, Lap Pui Chau

Research output: Journal article publication › Journal article › Academic research › peer-review

22 Citations (Scopus)

Abstract

This letter proposes a nonlinear L₁-norm approach for joint image registration and super-resolution (SR). Image SR is the fusion of multiple low-resolution (LR) images to produce a high-resolution (HR) image. Conventional SR algorithms are sensitive to the initial registration error and outliers in the LR images. In view of this, we present a new SR method to address these problems using L₁-norm optimization in joint image registration and HR image reconstruction. Experimental results show that the proposed method is effective in handling these issues in the HR image reconstruction.

Original language	English
Pages (from-to)	981-984
Number of pages	4
Journal	IEEE Signal Processing Letters
Volume	16
Issue number	11
DOIs	https://doi.org/10.1109/LSP.2009.2028106
Publication status	Published - Nov 2009
Externally published	Yes

Keywords

Image registration
image super-resolution
L-norm
nonlinear algorithm

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

More information

10.1109/LSP.2009.2028106

Cite this

@article{d26b9dd05bc9477bb9fde3562a0716e9,

title = "A Nonlinear L1-Norm Approach for Joint Image Registration and Super-Resolution",

abstract = "This letter proposes a nonlinear L1-norm approach for joint image registration and super-resolution (SR). Image SR is the fusion of multiple low-resolution (LR) images to produce a high-resolution (HR) image. Conventional SR algorithms are sensitive to the initial registration error and outliers in the LR images. In view of this, we present a new SR method to address these problems using L1-norm optimization in joint image registration and HR image reconstruction. Experimental results show that the proposed method is effective in handling these issues in the HR image reconstruction.",

keywords = "Image registration, image super-resolution, L-norm, nonlinear algorithm",

author = "Yap, \{Kim Hui\} and Yu He and Yushuang Tian and Chau, \{Lap Pui\}",

year = "2009",

month = nov,

doi = "10.1109/LSP.2009.2028106",

language = "English",

volume = "16",

pages = "981--984",

journal = "IEEE Signal Processing Letters",

issn = "1070-9908",

publisher = "IEEE",

number = "11",

}

TY - JOUR

T1 - A Nonlinear L1-Norm Approach for Joint Image Registration and Super-Resolution

AU - Yap, Kim Hui

AU - He, Yu

AU - Tian, Yushuang

AU - Chau, Lap Pui

PY - 2009/11

Y1 - 2009/11

N2 - This letter proposes a nonlinear L1-norm approach for joint image registration and super-resolution (SR). Image SR is the fusion of multiple low-resolution (LR) images to produce a high-resolution (HR) image. Conventional SR algorithms are sensitive to the initial registration error and outliers in the LR images. In view of this, we present a new SR method to address these problems using L1-norm optimization in joint image registration and HR image reconstruction. Experimental results show that the proposed method is effective in handling these issues in the HR image reconstruction.

AB - This letter proposes a nonlinear L1-norm approach for joint image registration and super-resolution (SR). Image SR is the fusion of multiple low-resolution (LR) images to produce a high-resolution (HR) image. Conventional SR algorithms are sensitive to the initial registration error and outliers in the LR images. In view of this, we present a new SR method to address these problems using L1-norm optimization in joint image registration and HR image reconstruction. Experimental results show that the proposed method is effective in handling these issues in the HR image reconstruction.

KW - Image registration

KW - image super-resolution

KW - L-norm

KW - nonlinear algorithm

UR - https://www.scopus.com/pages/publications/85008018511

U2 - 10.1109/LSP.2009.2028106

DO - 10.1109/LSP.2009.2028106

M3 - Journal article

AN - SCOPUS:85008018511

SN - 1070-9908

VL - 16

SP - 981

EP - 984

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

IS - 11

ER -