A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
Selection weighted vector directional filters
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Piecewise linear model-based image enhancement
EURASIP Journal on Applied Signal Processing
Enhancement of aerial images using threshold decomposition adaptive morphological filter
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Particle swarm based unsharp masking
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
| Hi-index | 0.01 |
A class of robust weighted median (WM) sharpening algorithms is developed in this paper. Unlike traditional linear sharpening methods, weighted median sharpeners are shown to be less sensitive to background random noise or to image artifacts introduced by JPEG and other compression algorithms. These concepts are extended to include data dependent weights under the framework of permutation weighted medians leading to tunable sharpeners that, in essence, are insensitive to noise and compression artifacts. Permutation WM sharpeners are subsequently generalized to smoother/sharpener structures that can sharpen edges and image details while simultaneously filter out background random noise. A statistical analysis of the various algorithms is presented, theoretically validating the characteristics of the proposed sharpening structures. A number of experiments are shown for the sharpening of JPEG compressed images and sharpening of images with background film-grain noise. These algorithms can prove useful in the enhancement of compressed or noisy images posted on the World Wide Web (WWW) as well as in other applications where the underlying images are unavoidably acquired with noise.