Q
Qingsong Zhu
Researcher at Chinese Academy of Sciences
Publications - 48
Citations - 2187
Qingsong Zhu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Image segmentation & Scale-space segmentation. The author has an hindex of 14, co-authored 47 publications receiving 1553 citations. Previous affiliations of Qingsong Zhu include Shenzhen University & The Chinese University of Hong Kong.
Papers
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Journal ArticleDOI
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior
TL;DR: A simple but powerful color attenuation prior for haze removal from a single input hazy image is proposed and outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.
Journal ArticleDOI
Estimation of Respiration Rate from Three-Dimensional Acceleration Data Based on Body Sensor Network
TL;DR: An adaptive band-pass filtering method combined with principal component analysis to derive the respiratory rate from three-dimensional acceleration data, using a body sensor network platform previously developed by us, and suggests that this method was capable of offering dynamic respiration rate estimation during various body activities.
Proceedings ArticleDOI
Single image dehazing using color attenuation prior
TL;DR: Experimental results show that the proposed approach is highly efficient and it outperforms state-of-the-art haze removal algorithms in terms of the dehazing effect as well.
Proceedings ArticleDOI
Evaluation of various speckle reduction filters on medical ultrasound images
TL;DR: A comparative study of seven filters, namely Lee, Frost, Median, Speckle Reduction Anisotropic Diffusion (SRAD), Perona-Malik's An isotropic diffusion (PMAD) filter, Spekle Reduction Bilateral Filter (SRBF) and Speckel Reduction filter based on soft thresholding in the Wavelet transform, to determine which despeckling algorithm is most effective and optimal for real-time implementation.
Journal ArticleDOI
Targeting Accurate Object Extraction From an Image: A Comprehensive Study of Natural Image Matting
TL;DR: A comprehensive survey of the existing image matting algorithms and compared using several metrics to demonstrate the strengths and weaknesses of each method both quantitatively and qualitatively is provided.