Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear image processing
Multi-Spectral Probabilistic Diffusion Using Bayesian Classification
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
Edge Detectors Based Anisotropic Diffusion for Enhancement of Digital Images
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Image Denoising by Scaled Bilateral Filtering
NCVPRIPG '11 Proceedings of the 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A universal noise removal algorithm with an impulse detector
IEEE Transactions on Image Processing
FSIM: A Feature Similarity Index for Image Quality Assessment
IEEE Transactions on Image Processing
Histogram Thresholding using Beam Theory and Ambiguity Measures
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
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A new denoising filter is proposed for human brain MR image. The proposed filter is based on the notion of existing bilateral filter whose objective is to get a noise-free smooth image, preserving edges and other features intact. We have introduced a weighing function that controls the impact of existing bilateral filter for denoising. It is conditioned by Rough Edge Map (REM) and Rough Class Label (RCL). The presence of noise makes difficult to get precise information of edge and class label. Rough Set Technique is expected to assign rough (imprecise) class label and edge label to the pixels in the given image. This function thus is expected to handle the impreciseness of edge and class label and thereby preserving these two by controlling the bilateral filter more efficiently. The filter is extensively applied on brain MR images. The current proposal is compared with some of state-of-the-art approaches using different image quality measures and found to be efficient in most of the cases.