A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Threshold Decomposition of Gray-Scale Morphology into Binary Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smart Interpolation by Anisotropic Diffusion
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Assorted pixels: multi-sampled imaging with structural models
ACM SIGGRAPH 2007 courses
Technical Section: Hyper-Resolution: Image detail reconstruction through parametric edges
Computers and Graphics
New edge-directed interpolation
IEEE Transactions on Image Processing
Image Superresolution Using Support Vector Regression
IEEE Transactions on Image Processing
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A new curve-fitting scheme is proposed in this paper to produce super-resolution images from a single low-resolution source image. The most unique feature of this method is that the threshold decomposition is performed on the given source image to obtain multiple binary images so that the curve-fitting applied on each resulted binary image can be made very efficient and accurate, thus allowing us to focus on tiny objects and thin structures so as to achieve rather nice visual results even when a large up-scaling factor is used. Two novel techniques are further proposed to improve the visual quality: (1) a spreading technique (applied on some significant pixels detected in each threshold decomposed binary image) is used to remove ladder-like false edges that often appear visually in super-resolution images, and (2) an edge correction (guided by the edge information extracted from the original source image) is used to sharpen all inherent edges. Our results are compared with those achieved by using the state-of-arts techniques, showing the ability of our algorithm to achieve a better visual quality in smooth areas as well as for sharp edges and small objects.