The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 영상 잡음 제거와 부분 공간 회귀 학습을 결합한 효과적인 저비트율 영상 복원 방법을 제안합니다. 제안된 프레임워크는 두 부분으로 구성됩니다. 기존 NLM 노이즈 제거를 통한 이미지 기본 구조 추정과 부분 공간 결합 회귀 학습을 통한 텍스처 구성 요소 예측입니다. 로컬 회귀 함수는 잡음이 제거된 패치에서 각 하위 공간의 원본 패치로 학습되며, 여기서 해당 압축 이미지 패치는 사전 학습 방식으로 앵커링 포인트를 생성하는 데 사용됩니다. 또한 더 강력한 결과를 얻기 위해 ESVR(Extreme Support Vector Regression)을 다변수 비선형 회귀로 확장합니다. 실험 결과는 제안한 방법이 다른 주요 방법에 비해 좋은 성능을 보인다는 것을 보여줍니다.
Zongliang GAN
Nanjing University of Posts and Telecommunications
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부
Zongliang GAN, "Low Bit-Rate Compression Image Restoration through Subspace Joint Regression Learning" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 10, pp. 2539-2542, October 2018, doi: 10.1587/transinf.2017EDL8278.
Abstract: In this letter, an effective low bit-rate image restoration method is proposed, in which image denoising and subspace regression learning are combined. The proposed framework has two parts: image main structure estimation by classical NLM denoising and texture component prediction by subspace joint regression learning. The local regression function are learned from denoised patch to original patch in each subspace, where the corresponding compression image patches are employed to generate anchoring points by the dictionary learning approach. Moreover, we extent Extreme Support Vector Regression (ESVR) as multi-variable nonlinear regression to get more robustness results. Experimental results demonstrate the proposed method achieves favorable performance compared with other leading methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8278/_p
부
@ARTICLE{e101-d_10_2539,
author={Zongliang GAN, },
journal={IEICE TRANSACTIONS on Information},
title={Low Bit-Rate Compression Image Restoration through Subspace Joint Regression Learning},
year={2018},
volume={E101-D},
number={10},
pages={2539-2542},
abstract={In this letter, an effective low bit-rate image restoration method is proposed, in which image denoising and subspace regression learning are combined. The proposed framework has two parts: image main structure estimation by classical NLM denoising and texture component prediction by subspace joint regression learning. The local regression function are learned from denoised patch to original patch in each subspace, where the corresponding compression image patches are employed to generate anchoring points by the dictionary learning approach. Moreover, we extent Extreme Support Vector Regression (ESVR) as multi-variable nonlinear regression to get more robustness results. Experimental results demonstrate the proposed method achieves favorable performance compared with other leading methods.},
keywords={},
doi={10.1587/transinf.2017EDL8278},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Low Bit-Rate Compression Image Restoration through Subspace Joint Regression Learning
T2 - IEICE TRANSACTIONS on Information
SP - 2539
EP - 2542
AU - Zongliang GAN
PY - 2018
DO - 10.1587/transinf.2017EDL8278
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2018
AB - In this letter, an effective low bit-rate image restoration method is proposed, in which image denoising and subspace regression learning are combined. The proposed framework has two parts: image main structure estimation by classical NLM denoising and texture component prediction by subspace joint regression learning. The local regression function are learned from denoised patch to original patch in each subspace, where the corresponding compression image patches are employed to generate anchoring points by the dictionary learning approach. Moreover, we extent Extreme Support Vector Regression (ESVR) as multi-variable nonlinear regression to get more robustness results. Experimental results demonstrate the proposed method achieves favorable performance compared with other leading methods.
ER -