D
Dan Feng
Researcher at Huazhong University of Science and Technology
Publications - 474
Citations - 8878
Dan Feng is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: RAID & Data deduplication. The author has an hindex of 39, co-authored 445 publications receiving 6643 citations. Previous affiliations of Dan Feng include Chinese Ministry of Education.
Papers
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Proceedings ArticleDOI
AOD-Net: All-in-One Dehazing Network
TL;DR: An image dehazing model built with a convolutional neural network (CNN) based on a re-formulated atmospheric scattering model, called All-in-One Dehazing Network (AOD-Net), which demonstrates superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality.
Journal ArticleDOI
Benchmarking Single-Image Dehazing and Beyond
TL;DR: In this article, the authors present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called Realistic Single-Image DEhazing (RESIDE).
Proceedings ArticleDOI
Performance impact and interplay of SSD parallelism through advanced commands, allocation strategy and data granularity
TL;DR: It is argued that, based on the results of the experimental study, it is these internal features and their interplay that will help provide the missing but significant insights to designing high-performance and high-endurance SSDs.
Proceedings ArticleDOI
CDRM: A Cost-Effective Dynamic Replication Management Scheme for Cloud Storage Cluster
TL;DR: This paper implemented CDRM in Hadoop Distributed File System (HDFS) and experiment results conclusively demonstrate that the CDRM is cost effective and outperforms default replication management of HDFS in terms of performance and load balancing for large-scale cloud storage.
Journal ArticleDOI
A Comprehensive Study of the Past, Present, and Future of Data Deduplication
Wen Xia,Hong Jiang,Dan Feng,Fred Douglis,Philip Shilane,Yu Hua,Min Fu,Yucheng Zhang,Yukun Zhou +8 more
TL;DR: The background and key features of data deduplication are reviewed, the main applications and industry trend are discussed, and the state-of-the-art research in data dedeplication is classified according to the key workflow of the data dedUplication process.