Learning An Explicit Weighting Scheme for Adapting Complex HSI Noise

Rui, Xiangyu; Cao, Xiangyong; Xie, Qi; Yue, Zongsheng; Zhao, Qian; Meng, Deyu

doi:10.1109/cvpr46437.2021.00667

Cited by 13 publications

(3 citation statements)

References 44 publications

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“…Rui et al. [50] presented a new scheme for hyperspectral image denoising, this scheme adopts the general weight principle in a data‐driven way, instead of weighting different pixels of hyperspectral images to suppress the negative effects of noise elements.…”

Section: Image Denoising Methodsmentioning

confidence: 99%

“…Moseley et al [49] proposed a denoising method for extremely low-light images of permanent shadowed regions (PSRs) of the lunar surface taken by the narrow-angle camera on the Lunar Reconnaissance Orbiter satellite. Rui et al [50] presented a new scheme for hyperspectral image denoising, this scheme adopts the general weight principle in a data-driven way, instead of weighting different pixels of hyperspectral images to suppress the negative effects of noise elements.…”

Section: Dnn Denoising Models For Special Scenesmentioning

confidence: 99%

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Recent progress in image denoising: A training strategy perspective

Chen

Xiang

et al. 2023

IET Image Processing

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Image denoising is one of the hottest topics in image restoration area, it has achieved great progress both in terms of quantity and quality in recent years, especially after the wide and intensive application of deep neural networks. In many deep learning based image denoising models, the performance can greatly benefit from the prepared clean/noisy image pairs used for model training, however, it also limits the application of these models in real denoising scenes. Therefore, more and more researchers tend to develop models that can be learned without image pairs, namely the denoising models that can be well generalised in real‐world denoising tasks. This motivates to make a survey on the recent development of image denoising methods. In this paper, the typical denoising methods from the perspective of model training are reviewed, the reviewed methods are categorised into four classes: the models need clean/noisy image pairs to train, the models trained on multiple noisy images, the models can be learned from a single noisy image, and the visual transformer based models. The denoising results of different denoisers were compared on some public datasets to discover the performance and advantages. The challenges and future directions in image denoising area are also discussed.

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Section: Image Denoising Methodsmentioning

confidence: 99%

Section: Dnn Denoising Models For Special Scenesmentioning

confidence: 99%

Recent progress in image denoising: A training strategy perspective

Chen

Xiang

et al. 2023

IET Image Processing

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“…NER is a text processing technique that recognises words belonging to specific Named Entity categories ( 25 ). This is a crucial tool in NLP to extract information from documents.…”

Section: Mathematical Modelmentioning

confidence: 99%

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

Bhatia

Alojail

Sengan

et al. 2022

Front. Public Health

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Handwritten prescriptions and radiological reports: doctors use handwritten prescriptions and radiological reports to give drugs to patients who have illnesses, injuries, or other problems. Clinical text data, like physician prescription visuals and radiology reports, should be labelled with specific information such as disease type, features, and anatomical location for more effective use. The semantic annotation of vast collections of biological and biomedical texts, like scientific papers, medical reports, and general practitioner observations, has lately been examined by doctors and scientists. By identifying and disambiguating references to biomedical concepts in texts, medical semantics annotators could generate such annotations automatically. For Medical Images (MedIMG), we provide a methodology for learning an effective holistic representation (handwritten word pictures as well as radiology reports). Deep Learning (DL) methods have recently gained much interest for their capacity to achieve expert-level accuracy in automated MedIMG analysis. We discovered that tasks requiring significant responsive fields are ideal for downscaled input images that are qualitatively verified by examining functional, responsive areas and class activating maps for training models. This article focuses on the following contributions: (a) Information Extraction from Narrative MedImages, (b) Automatic categorisation on image resolution with an impact on MedIMG, and (c) Hybrid Model to Predictions of Named Entity Recognition utilising RNN + LSTM + GRM that perform admirably in every trainee for every input purpose. At the same time, supplying understandable scale weight implies that such multi-scale structures are also crucial for extracting information from high-resolution MedIMG. A portion of the reports (30%) are manually evaluated by trained physicians, while the rest were automatically categorised using deep supervised training models based on attention mechanisms and supplied with test reports. MetaMapLite proved recall and precision, but also an F1-score equivalent for primary biomedicine text search techniques and medical text examination on many databases of MedIMG. In addition to implementing as well as getting the requirements for MedIMG, the article explores the quality of medical data by using DL techniques for reaching large-scale labelled clinical data and also the significance of their real-time efforts in the biomedical study that have played an instrumental role in its extramural diffusion and global appeal.

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