http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Jiacen (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
-
A Method of Coupling Expected Patch Log Likelihood and Guided Filtering for Image De-noising
( Shunfeng Wang ),( Jiacen Xie ),( Yuhui Zheng ),( Jin Wang ),( Tao Jiang ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.2
With the advent of the information society, image restoration technology has aroused considerable interest. Guided image filtering is more effective in suppressing noise in homogeneous regions, but its edge-preserving property is poor. As such, the critical part of guided filtering lies in the selection of the guided image. The result of the Expected Patch Log Likelihood (EPLL) method maintains a good structure, but it is easy to produce the ladder effect in homogeneous areas. According to the complementarity of EPLL with guided filtering, we propose a method of coupling EPLL and guided filtering for image de-noising. The EPLL model is adopted to construct the guided image for the guided filtering, which can provide better structural information for the guided filtering. Meanwhile, with the secondary smoothing of guided image filtering in image homogenization areas, we can improve the noise suppression effect in those areas while reducing the ladder effect brought about by the EPLL. The experimental results show that it not only retains the excellent performance of EPLL, but also produces better visual effects and a higher peak signal-to-noise ratio by adopting the proposed method.
-
A Method of Coupling Expected Patch Log Likelihood and Guided Filtering for Image De-noising
Wang, Shunfeng,Xie, Jiacen,Zheng, Yuhui,Wang, Jin,Jiang, Tao Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.2
With the advent of the information society, image restoration technology has aroused considerable interest. Guided image filtering is more effective in suppressing noise in homogeneous regions, but its edge-preserving property is poor. As such, the critical part of guided filtering lies in the selection of the guided image. The result of the Expected Patch Log Likelihood (EPLL) method maintains a good structure, but it is easy to produce the ladder effect in homogeneous areas. According to the complementarity of EPLL with guided filtering, we propose a method of coupling EPLL and guided filtering for image de-noising. The EPLL model is adopted to construct the guided image for the guided filtering, which can provide better structural information for the guided filtering. Meanwhile, with the secondary smoothing of guided image filtering in image homogenization areas, we can improve the noise suppression effect in those areas while reducing the ladder effect brought about by the EPLL. The experimental results show that it not only retains the excellent performance of EPLL, but also produces better visual effects and a higher peak signal-to-noise ratio by adopting the proposed method.
-
A New Operator Extracting Image Patch Based on EPLL
Jianwei Zhang,Tao Jiang,Yuhui Zheng,Jin Wang,Jiacen Xie 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.3
Multivariate finite mixture model is becoming more and more popular in image processing. Performingimage denoising from image patches to the whole image has been widely studied and applied. However, thereremains a problem that the structure information is always ignored when transforming the patch into thevector form. In this paper, we study the operator which extracts patches from image and then transformsthem to the vector form. Then, we find that some pixels which should be continuous in the image patches arediscontinuous in the vector. Due to the poor anti-noise and the loss of structure information, we propose anew operator which may keep more information when extracting image patches. We compare the newoperator with the old one by performing image denoising in Expected Patch Log Likelihood (EPLL) method,and we obtain better results in both visual effect and the value of PSNR.
-
A New Operator Extracting Image Patch Based on EPLL
Zhang, Jianwei,Jiang, Tao,Zheng, Yuhui,Wang, Jin,Xie, Jiacen Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.3
Multivariate finite mixture model is becoming more and more popular in image processing. Performing image denoising from image patches to the whole image has been widely studied and applied. However, there remains a problem that the structure information is always ignored when transforming the patch into the vector form. In this paper, we study the operator which extracts patches from image and then transforms them to the vector form. Then, we find that some pixels which should be continuous in the image patches are discontinuous in the vector. Due to the poor anti-noise and the loss of structure information, we propose a new operator which may keep more information when extracting image patches. We compare the new operator with the old one by performing image denoising in Expected Patch Log Likelihood (EPLL) method, and we obtain better results in both visual effect and the value of PSNR.
KCI
KISS연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
