Many existing methods effectively remove impulse noise from images, but they usually perform well only in a certain noise level (i.e., either low or high contamination). An impulse noise detection technique that is based on mean shift filtering and an effective vector filtering method that is based on channel suppression processing are proposed. The proposed denoising method excellently suppresses impulse noise in color images and performs well in both low and medium noise contamination cases. First, the noise detection method is performed to divide image pixels into noisy and possibly noise-free ones. Then, for the noisy pixels detected, according to the contamination levels, different denoising strategies are employed. For slightly corrupted images, the proposed channel-suppressed vector filter is performed, whereas for medially and highly contaminated images, a total variation denoising technique is applied, followed by a further impulse noise detection and channel-suppressed filtering. Finally, for the possible noise-free pixels, fine noise detection and channel-suppressed filtering are performed. Extensive simulation results exhibit the validity of the proposed solution by showing clear performance improvements over other widely used color image filtering methods.
Images captured by mobile phone cameras via pipeline processing usually contain various kinds of noises, especially
granular noise with different shapes and sizes in both luminance and chrominance channels. In chrominance channels,
noise is closely related to image brightness. To improve image quality, this paper presents a new method to denoise such
mobile phone images. The proposed scheme converts the noisy RGB image to luminance and chrominance images,
which are then denoised by a common filtering framework. The common filtering framework processes a noisy pixel by
first excluding the neighborhood pixels that significantly deviate from the (vector) median and then utilizing the other
neighborhood pixels to restore the current pixel. In the framework, the strength of chrominance image denoising is
controlled by image brightness. The experimental results show that the proposed method obviously outperforms some
other representative denoising methods in terms of both objective measure and visual evaluation.
It is difficult to precisely detect all impulsive noise in color images due to the nonstationarity caused by edges and fine details. For many pixels, we can not absolutely classify them as noisy or noise-free, but can only describe them using the likelihood that they are corrupted by impulsive noise. Based on this consideration, we present a new filtering solution to removing impulsive noise from color images. The proposed method first utilizes the unit transforms of quaternions to represent the chromaticity difference of two color pixels, and then divides the image into noise-free and possible noisy pixels. Finally it performs adaptive weighted vector median filtering operations on only the possible noisy pixels to suppress noise. The new weighting mechanism is based on a joint spatial/quaternion-chromaticity criterion, which ensures that pixels with different contamination likelihoods have different contributions to the filter's output. The extensive simulation results indicate that the proposed method significantly outperforms some other well-known multichannel filtering techniques.
We present a new impulsive noise removal method, which combines a switching mechanism and adaptive weighted median filtering techniques. By utilizing four Laplacian operators and median-based comparison techniques to classify the image pixels into thin-line pixels, noise-free pixels, and noisy ones, the developed solution applies adaptive weighted median filtering operations only in the detected noisy locations to suppress impulsive noise, and keeps the other pixels unchanged. The simulation results exhibit the excellent performance gains of the proposed solution in suppressing impulsive noise with different contamination ratios over other prior-art methods in terms of both objective measurements and visual image quality.
A new impulsive noise removal algorithm, the selective adaptive weighted median filter (SAWMF), is introduced. The proposed solution is a class of adaptive weighted median filters with incorporation of a switching mechanism. Using a median-based comparison technique to classify each image pixel as an impulse or a noise-free one, the new algorithm employs a weighted median filter where the weights are adaptively selected from two fixed values to restore the detected noisy pixels and keep the noise-free ones unchanged. The experimental results indicate that the SAWMF provides a significant performance improvement over many of the existing filtering techniques in suppressing impulsive noise with different contamination ratios.
KEYWORDS: Image filtering, Digital filtering, Optical filters, Switching, RGB color model, Image quality, Color difference, Visualization, Image processing, Space operations
This paper introduces a new class of switching vector median filter. The proposed algorithm first uses four directional
masks to analyze the color difference between the central pixel and its neighborhood pixels in the RGB color space and
classify each color pixel into noisy pixel or noise-free one, and then employs the standard vector median filtering
operations in the detected noisy locations to restore the corrupted pixels and leave the noise-free ones unchanged. The
simulation results show that the proposed method excellently suppresses impulsive noise as well as preserving the image
details well, and significantly outperforms the existing vector filtering solutions in terms of both the objective measures
and the perceptual visual quality.
By combining the median characteristics of the neighboring pixels around the interpolation points and their spatial
information, a novel image interpolation algorithm is introduced in this paper. The proposed interpolator first utilizes
both the aggregated gray differences and the spatial distances to compute the weights associated with the neighboring
pixels and then employs a data-adaptive filter to estimate the interpolated pixels. The experimental results demonstrate
the validity of the proposed interpolator by showing significant performance improvements against the conventional
interpolation methods.
This paper introduces a new class of weighted median filters. The proposed algorithm employs a spatial distance weighting function, which is based on the conclusion that the degree of similarity between two stimuli can be quantified as a simple exponential decay function of a normalized distance in a psychological space. Depending on two parameters of the weighting function, the proposed approach can provide filtering performance ranging from an identity operation to that of the standard median filter (SMF), and by adaptively adjusting one of the parameters, the best possible filtering effect may be achieved. The experimental results show the supriority of the proposed solution to the SMF and some other median filtering methods in terms of both the objective measures and the perceptual visual quality.
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