On some results in fuzzy metric spaces
Fuzzy Sets and Systems
Efficient Modification of the Central Weighted Vector Median Filter
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Adaptive vector median filtering
Pattern Recognition Letters
Adaptive Color Image Filtering Based on Center-Weighted Vector Directional Filters
Multidimensional Systems and Signal Processing
Modified switching median filter for impulse noise removal
Signal Processing
Quaternion switching filter for impulse noise reduction in color image
Signal Processing
Fast adaptive optimization of weighted vector median filters
IEEE Transactions on Signal Processing
Directional processing of color images: theory and experimental results
IEEE Transactions on Image Processing
Generalized multichannel image-filtering structures
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
IEEE Transactions on Image Processing
Partition-based vector filtering technique for suppression of noise in digital color images
IEEE Transactions on Image Processing
Three-dimensional median-related filters for color image sequence filtering
IEEE Transactions on Circuits and Systems for Video Technology
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An impulse noise removal method based on noise detection and image edge detection is proposed to improve the performance of vector median filter. Corrupted pixels are first discriminated from the noise-free pixels by comparing the current pixel with the corresponding pixel in a reference image. Then corrupted pixels are filtered by the proposed weighted vector median filter. The novelty of this filter lies in its weighting technique based on image edge detection. The weight of each pixel is determined by its group which is related to the image edge. This method can suppress noise and reduce edge blurring effectively. Experimental results show that the proposed method outperforms all algorithms examined in this paper in terms of MAE, MSE and PSNR values.