Fundamentals of digital image processing
Fundamentals of digital image processing
The nature of statistical learning theory
The nature of statistical learning theory
Removing Noise and Preserving Details with Relaxed Median Filters
Journal of Mathematical Imaging and Vision
Digital Image Processing
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A qualitative profile-based approach to edge detection
A qualitative profile-based approach to edge detection
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Removing impulse bursts from images by training-based filtering
EURASIP Journal on Applied Signal Processing
Vector median filter for removal of impulse noise from color images
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Missing lines recovery and impulse noise suppression using improved 2-D median filters
IEEE Transactions on Consumer Electronics
A real-time 2-D median based filter for video signals
IEEE Transactions on Consumer Electronics
Adaptive scratch noise filtering
IEEE Transactions on Consumer Electronics
Modified 2D median filter for impulse noise suppression in a real-time system
IEEE Transactions on Consumer Electronics
Noise adaptive soft-switching median filter
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Image information restoration based on long-range correlation
IEEE Transactions on Circuits and Systems for Video Technology
A unified approach for determining the underlying causes of non-stationary disturbances
International Journal of Computer Applications in Technology
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In this paper, we propose a new method to solve the problem of fixed-valued impulsive noise reduction in images. Nonlinear filter like the median filter (MF) is useful for reducing random noise and periodical patterns, but direct median filtering have undesirable side effects such as smoothening of noise free regions, which results in loss of image detail and distortion of the signal. Impulse noise is suppressed by selectively filtering the contaminated signal regions only, thus minimizing distortion of clean passages and loss of high frequencies. In the first phase, support vector machines (SVM) are used to segment the set of pixels N that are likely to be contaminated by the mixed impulses. In the second phase, the image is restored by employing a combination of the best neighborhood match filter (BNM) and the modified multi-shell median filter (MMMF) to these segmented regions. This method combines the effectiveness of the best neighborhood matching (BNM) filter in suppression of the noise components while adapting itself to the local image structures, and the edge and finer image detail preserving characteristics of the MMMF. To support our proposed method, numerical results are also provided, which indicate that the filter is extremely useful for preserving edges or monotonic changes in trend, while eliminating short duration impulses of high density.