Cargando…

ADE-CycleGAN: A Detail Enhanced Image Dehazing CycleGAN Network

The preservation of image details in the defogging process is still one key challenge in the field of deep learning. The network uses the generation of confrontation loss and cyclic consistency loss to ensure that the generated defog image is similar to the original image, but it cannot retain the d...

Descripción completa

Detalles Bibliográficos
Autores principales:	Yan, Bingnan, Yang, Zhaozhao, Sun, Huizhu, Wang, Conghui
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054719/ https://www.ncbi.nlm.nih.gov/pubmed/36992005 http://dx.doi.org/10.3390/s23063294

Ejemplares similares

Unsupervised Dark-Channel Attention-Guided CycleGAN for Single-Image Dehazing
por: Chen, Jiahao, et al.
Publicado: (2020)

Enhanced CycleGAN Network with Adaptive Dark Channel Prior for Unpaired Single-Image Dehazing
por: Xu, Yijun, et al.
Publicado: (2023)

CycleGAN for interpretable online EMT compensation
por: Krumb, Henry, et al.
Publicado: (2021)

Comparative analysis of CycleGAN and AttentionGAN on face aging application
por: Sharma, Neha, et al.
Publicado: (2022)

Effective Immunohistochemistry Pathology Microscopy Image Generation Using CycleGAN
por: Xu, Zidui, et al.
Publicado: (2020)

Digital Image Art Style Transfer Algorithm Based on CycleGAN
por: Fu, Xuhui
Publicado: (2022)

Application of Improved CycleGAN in Laser-Visible Face Image Translation
por: Qin, Mingyu, et al.
Publicado: (2022)

Image Translation of Breast Ultrasound to Pseudo Anatomical Display by CycleGAN
por: Barkat, Lilach, et al.
Publicado: (2023)

Mol-CycleGAN: a generative model for molecular optimization
por: Maziarka, Łukasz, et al.
Publicado: (2020)

Spectral Normalized CycleGAN with Application in Semisupervised Semantic Segmentation of Sonar Images
por: Zhang, Zhisheng, et al.
Publicado: (2022)

Tackling stain variability using CycleGAN-based stain augmentation
por: Bouteldja, Nassim, et al.
Publicado: (2022)

A New Method for Training CycleGAN to Enhance Images of Cold Seeps in the Qiongdongnan Sea
por: Li, Yuanheng, et al.
Publicado: (2023)

Study of low-dose PET image recovery using supervised learning with CycleGAN
por: Zhao, Kui, et al.
Publicado: (2020)

Depth image conversion model based on CycleGAN for growing tomato truss identification
por: Jung, Dae-Hyun, et al.
Publicado: (2022)

Laser-Visible Face Image Translation and Recognition Based on CycleGAN and Spectral Normalization
por: Qin, Mingyu, et al.
Publicado: (2023)

Development of Road Surface Detection Algorithm Using CycleGAN-Augmented Dataset
por: Choi, Wansik, et al.
Publicado: (2021)

A novel approach for eliminating metal artifacts based on MVCBCT and CycleGAN
por: Cao, Zheng, et al.
Publicado: (2022)

Decomposition of musculoskeletal structures from radiographs using an improved CycleGAN framework
por: Nakanishi, Naoki, et al.
Publicado: (2023)

Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN
por: Liang, Zhaohui, et al.
Publicado: (2022)

Assessment of hyperspectral imaging and CycleGAN-simulated narrowband techniques to detect early esophageal cancer
por: Yang, Kai-Yao, et al.
Publicado: (2023)

Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning()
por: Ghassemi, Navid, et al.
Publicado: (2023)

Improving de novo Molecule Generation by Embedding LSTM and Attention Mechanism in CycleGAN
por: Wang, Feng, et al.
Publicado: (2021)

Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
por: Sandfort, Veit, et al.
Publicado: (2019)

Short-Axis PET Image Quality Improvement by Attention CycleGAN Using Total-Body PET
por: Shang, Chong, et al.
Publicado: (2022)

Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN
por: Connell, Marc, et al.
Publicado: (2022)

Semi-supervised method for image texture classification of pituitary tumors via CycleGAN and optimized feature extraction
por: Zhu, Hong, et al.
Publicado: (2020)

Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images
por: Bargshady, Ghazal, et al.
Publicado: (2022)

Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy
por: Sun, Hongfei, et al.
Publicado: (2021)

Frequency-Domain-Based Structure Losses for CycleGAN-Based Cone-Beam Computed Tomography Translation
por: Pai, Suraj, et al.
Publicado: (2023)

Generating Defective Epoxy Drop Images for Die Attachment in Integrated Circuit Manufacturing via Enhanced Loss Function CycleGAN
por: Alam, Lamia, et al.
Publicado: (2023)

How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study
por: Sarv Ahrabi, Sima, et al.
Publicado: (2022)

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach
por: Aguirre, Nicolas, et al.
Publicado: (2023)

CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma
por: Jihong, Chen, et al.
Publicado: (2023)

Semi-supervised CycleGAN for domain transformation of chest CT images and its application to opacity classification of diffuse lung diseases
por: Mabu, Shingo, et al.
Publicado: (2021)

SR-CycleGAN: super-resolution of clinical CT to micro-CT level with multi-modality super-resolution loss
por: Zheng, Tong, et al.
Publicado: (2022)

Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis
por: Sato, Junya, et al.
Publicado: (2023)

A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality
por: Yang, Bo, et al.
Publicado: (2022)

Erratum: A comparison study between CNN-based deformed planning CT and CycleGAN-based synthetic CT methods for improving iCBCT image quality
Publicado: (2023)

A denoising method based on cyclegan with attention mechanisms for improving the hidden distress features of pavement
por: Liu, Lei, et al.
Publicado: (2023)

CT-Based Pelvic T(1)-Weighted MR Image Synthesis Using UNet, UNet++ and Cycle-Consistent Generative Adversarial Network (Cycle-GAN)
por: Kalantar, Reza, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_2vg60tg3trh6tap79v7m69bieh