A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast edge-oriented algorithm for image interpolation
Image and Vision Computing
New edge-directed interpolation
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
An edge-guided image interpolation algorithm via directional filtering and data fusion
IEEE Transactions on Image Processing
FSIM: A Feature Similarity Index for Image Quality Assessment
IEEE Transactions on Image Processing
Real-Time Artifact-Free Image Upscaling
IEEE Transactions on Image Processing
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Many interpolation methods have been developed for high visual quality, but fail for preserving image structures. Edges carry heavy structural messages for visual tasks. Importance of edge preservation imposes edge-directed interpolation (EDI) methods a center of focus. How to measure edge-preserving ability has not been mentioned. In this paper, two metrics are proposed to measure the ability by edge-preserving ratio from accuracy and robustness. Performance of four edge-directed interpolation with two traditional methods are evaluated on two groups of standard images with other six commonly-used metrics. Experimental results show that EDI methods are better than traditional methods with highly improved edge-preserving ratio.