C
Caiming Zhang
Researcher at Shandong University
Publications - 269
Citations - 3250
Caiming Zhang is an academic researcher from Shandong University. The author has contributed to research in topics: Interpolation & Image segmentation. The author has an hindex of 21, co-authored 241 publications receiving 2047 citations. Previous affiliations of Caiming Zhang include Shandong University of Finance and Economics & Shandong Institute of Business and Technology.
Papers
More filters
Journal ArticleDOI
Brief review of image denoising techniques
TL;DR: This paper gives the formulation of the image denoising problem, and then it presents several imageDenoising techniques, which discuss the characteristics of these techniques and provide several promising directions for future research.
Journal ArticleDOI
An Efficient SVD-Based Method for Image Denoising
TL;DR: The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.
Journal ArticleDOI
Single-Image Super-Resolution Based on Rational Fractal Interpolation
TL;DR: A novel single-image super-resolution procedure, which upscales a given low-resolution input image to a high-resolution image while preserving the textural and structural information, and develops a single- image SR algorithm based on the proposed model.
Journal ArticleDOI
Adaptive knot placement using a GMM-based continuous optimization algorithm in B-spline curve approximation
TL;DR: The knots of a parametric B-spline curve were treated as variables, and the initial location of every knot was generated using the Monte Carlo method in its solution domain to achieve better approximation accuracy than methods based on artificial immune system, genetic algorithm or squared distance minimization.
Journal ArticleDOI
Patch-Based Image Inpainting via Two-Stage Low Rank Approximation
TL;DR: A two-stage low rank approximation (TSLRA) scheme is designed to recover image structures and refine texture details of corrupted images, which is comparable and even superior to some state-of-the-art inpainting algorithms.