Computer and Robot Vision
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Image Interpolation using Mathematical Morphology
DIAL '06 Proceedings of the Second International Conference on Document Image Analysis for Libraries
Majority ordering and the morphological pattern spectrum
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
New edge-directed interpolation
IEEE Transactions on Image Processing
Adaptively quadratic (AQua) image interpolation
IEEE Transactions on Image Processing
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When magnifying a bitmapped image, we want to increase the number of pixels it covers, allowing for finer details in the image, which are not visible in the original image. Simple interpolation techniques are not suitable because they introduce jagged edges, also called “jaggies”. Earlier we proposed the “mmint” magnification method (for integer scaling factors), which avoids jaggies. It is based on mathematical morphology. The algorithm detects jaggies in magnified binary images (using pixel replication) and removes them, making the edges smoother. This is done by replacing the value of specific pixels. In this paper, we extend the binary mmint to greyscale images. The pixels are locally binarized so that the same morphological techniques can be applied as for mmint. We take care of the more difficult replacement of pixel values, because several grey values can be part of a jaggy. We then discuss the visual results of the new greyscale method.