Zobrazeno 1 - 10
of 150
pro vyhledávání: '"Qiegen Liu"'
Autor:
Kangjun Guo, Zhiyuan Zheng, Wenhua Zhong, Zilong Li, Guijun Wang, Jiahong Li, Yubin Cao, Yiguang Wang, Jiabin Lin, Qiegen Liu, Xianlin Song
Publikováno v:
Photoacoustics, Vol 38, Iss , Pp 100623- (2024)
Photoacoustic tomography (PAT) regularly operates in limited-view cases owing to data acquisition limitations. The results using traditional methods in limited-view PAT exhibit distortions and numerous artifacts. Here, a novel limited-view PAT recons
Externí odkaz:
https://doaj.org/article/f1a5bb2ffebb4fb9a4380510b071ab44
Autor:
Wenhua Zhong, Tianle Li, Shangkun Hou, Hongyu Zhang, Zilong Li, Guijun Wang, Qiegen Liu, Xianlin Song
Publikováno v:
Photoacoustics, Vol 38, Iss , Pp 100613- (2024)
Traditional methods under sparse view for reconstruction of photoacoustic tomography (PAT) often result in significant artifacts. Here, a novel image to image transformation method based on unsupervised learning artifact disentanglement network (ADN)
Externí odkaz:
https://doaj.org/article/33b572b22ed742a8901512c7f81e421b
Autor:
Xianlin Song, Xueyang Zou, Kaixin Zeng, Jiahong Li, Shangkun Hou, Yuhua Wu, Zilong Li, Cheng Ma, Zhiyuan Zheng, Kangjun Guo, Qiegen Liu
Publikováno v:
Photoacoustics, Vol 40, Iss , Pp 100646- (2024)
Photoacoustic tomography (PAT) is an innovative biomedical imaging technology, which has the capacity to obtain high-resolution images of biological tissue. In the extremely limited-view cases, traditional reconstruction methods for photoacoustic tom
Externí odkaz:
https://doaj.org/article/ebc720056f2240a0aa66c898eaea8c4f
Publikováno v:
Photonics, Vol 11, Iss 4, p 388 (2024)
In digital holography, reconstructed image quality can be primarily limited due to the inability of a single small aperture sensor to cover the entire field of a hologram. The use of multi-sensor arrays in synthetic aperture digital holographic imagi
Externí odkaz:
https://doaj.org/article/c802e62c0f8d4471bd5f2eb54ff6d5ac
Autor:
Xianlin Song, Guijun Wang, Wenhua Zhong, Kangjun Guo, Zilong Li, Xuan Liu, Jiaqing Dong, Qiegen Liu
Publikováno v:
Photoacoustics, Vol 33, Iss , Pp 100558- (2023)
As a non-invasive hybrid biomedical imaging technology, photoacoustic tomography combines high contrast of optical imaging and high penetration of acoustic imaging. However, the conventional standard reconstruction under sparse view could result in l
Externí odkaz:
https://doaj.org/article/710ae618ed974f048b14a44103560bb7
Publikováno v:
CT Lilun yu yingyong yanjiu, Vol 31, Iss 6, Pp 709-720 (2022)
Reducing the dose of computed tomography (CT) is essential for reducing the radiation risk in clinical applications. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT im
Externí odkaz:
https://doaj.org/article/147f1430256c4e699e5b736791fd1a00
Publikováno v:
IEEE Access, Vol 7, Pp 150082-150092 (2019)
Image restoration is an extensively studied area with lots of outstanding algorithms developed. Nevertheless, most existing methods still have some limitations that only apply to a single tailored restoration task or suffer from long iterative recons
Externí odkaz:
https://doaj.org/article/2afa549fdf0e4c0497edd9e2242f7b18
Publikováno v:
IEEE Access, Vol 7, Pp 178486-178495 (2019)
Lesion segmentation is of great research interest due to its capability in facilitating accurate stroke diagnosis and surgical planning. Existing deep neural networks, such as U-net, have demonstrated encouraging progress in biomedical image segmenta
Externí odkaz:
https://doaj.org/article/62f9dc370305405cb30b7c953d702950
Publikováno v:
IEEE Access, Vol 6, Pp 6303-6315 (2018)
This paper proposes a detail-preserving image denoising method via cluster-wise progressive principal component analysis (PCA) thresholding based on the Marchenko-Pastur (MP) law in random matrix theory. According to random matrix theory, an efficien
Externí odkaz:
https://doaj.org/article/c4233036215148fe9182e428675cfd69
Publikováno v:
International Journal of Biomedical Imaging, Vol 2016 (2016)
Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularizati
Externí odkaz:
https://doaj.org/article/6e71330ae03c4fccaf7df632b9981498