Impulsive Noise In Color Images Research Articles

Genetic Programming to Remove Impulse Noise in Color Images

This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image channels. The correction stage consists of a vector median filter version that modifies color channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to color image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment.

A New Adaptive Filter to Remove Impulsive Noise in Color Images

In this paper, a new adaptive vector directional distance filter (AVDDF) is proposed. The AVDDF filter is developed to remove the ‘salt and papper’ impulsive noise in color images. This algorithm consists in first step to detect pixels that are likely to have been contaminated with noise by using a threshold value. In second step, after recognizing the corrupted pixels, the smallest angular‐magnitude distance is used to replace the noisy pixels. The proposed filter is tested with several standard color images which are contaminated with various levels of ‘salt and pepper’ impulsive noise (3%, 5%, 10%, 20% and 30%). The performance of the proposed filter is measured with peak signal‐to‐noise ratio (PSNR) and normalized color difference (NCD). The simulation results show that the proposed filter permits to ameliorate the performance of the classic vector directional distance filter (VDDF). Indeed, it provides an improvement by an average of 5% and 26% in the PSNR and NCD relative to the VDDF filter, respectively, with a small increase in the execution time by 6%. Besides, the AVDDF filter allows also an enhancement by an average of 2% and 14% in the PSNR and NCD relative to adaptive vector median filter (AVMF), respectively. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

On the application of the reachability distance in the suppression of mixed Gaussian and impulsive noise in color images

In this paper, we address the problem of mixed Gaussian and impulsive noise reduction in color images. A robust filtering technique is proposed, which is utilizing a novel concept of pixels dissimilarity based on the reachability distance. The structure of the denoising method requires the estimation of the impulsiveness of each pixel in the processing block using the introduced local reachability concept. Furthermore, we determine the similarity of each pixel in the block to the central patch consisting of the processed pixel and its neighbors. Both measures are calculated as an average of modified reachability distances to the most similar pixels of the central patch and the final filtering output is a weighted average of all pixels belonging to the processing block. The proposed technique was compared with widely used filtering methods and the performed experiments proved its satisfying denoising properties. The introduced filtering design is insensitive to outliers and their clusters introduced by the impulsive noise process, preserves details and is able to efficiently suppress the Gaussian noise while enhancing the image edges. Additionally, we proposed a method which estimates the noise contamination intensity, so that the proposed filter is able to adaptively tune its parameters.

Denoising Color Images Based on Local Orientation Estimation and CNN Classifier

A structure-adaptive vector filter for removal of impulse noise from color images is presented. The proposed method is based on local orientation estimation. A color image is represented in quaternion form, and then, quaternion Fourier transform is used to compute the orientation of the pattern in a local neighborhood. Since the computation in quaternion frequency domain is extremely time-consuming, we prove a theorem that the integral of the product of frequency variables and the magnitude of quaternion frequency signals can be computed directly in spatial domain, which results that the color orientation detection problem can be solved in spatial domain. Based on the local orientation and orientation strength, the size, shape, and orientation of the support window of vector median filter (VMF) are adaptively determined, leading to an effective structure-adaptive VMF. Unlike the classical VMF restricting the output to the existing color samples, this paper computes the output of VMF over the entire 3D data space, which boosts the filtering performance effectively. To further improve denoising effect, a deep convolutional neural network is employed to detect impulse noise in color images and integrated into the proposed denoising framework. The experimental results exhibit the effectiveness of the proposed denoiser by showing significant performance improvements both in noise suppression and in detail preservation, compared to other color image denoising methods.

Algorithm for Estimating Type and Probability of Impulse Noise in Color Images

Abstract—Color digital images are often contaminated by impulse noise during transmitting and recording of information. Efficient methods for elimination of impulse noise are needed to improve image quality. An algorithm for estimating of the type and probability of impulse noise in color images using morphological operations is proposed. Results of computer experiments are presented to demonstrate the performance of the proposed algorithm for estimating of the type and probability of impulse noise for several interference models.

Removal of impulse noise in color images based on convolutional neural network

A new denoising framework based on deep convolutional neural network for suppressing impulse noise in color images is proposed in this paper. The proposed framework consists of two modules: noise detection and image reconstruction, both of which are implemented by a deep convolutional neural network. First, a noise classifier network is trained to detect random-valued impulse noise in a color image, which not only can detect the noisy color vector pixels but also can further identify the corrupted channels of each noisy color pixel. Then, a sparse clean color image is computed by replacing the values of noisy channels with 0 and keeping other noise-free channels unchanged. Finally, the sparse clean color image is fed to another denoiser network to reconstruct the denoised image. Experimental results show that the proposed denoiser outperforms other state-of-the-art methods clearly in both performance measure and visual evaluation.

A two-stage switching vector median filter based on quaternion for removing impulse noise in color images

This paper presents a novel two-stage filtering algorithm for removing impulse noise in color images. Quaternion theory is used to represent the intensity and chromaticity differences of two color pixels. Use of quaternion treats color pixels as vectors and processes color images as single unit rather than as separated color components. This preserves the existing correlation and three dimensional vector natures of the color channels. In the first stage of noise detection, the color pixels are sorted and assigned a rank based on the aggregated sum of color pixel differences with other pixels inside the filtering window. The central pixel is considered as probably corrupted by an impulse if its rank is bigger than a predefined rank. In the second stage, the probably corrupted candidate is again checked for an edge or an impulse by using four Laplacian convolution kernels. If the minimum difference of these four convolution is larger than a predefined threshold, then the central pixel is regarded as an impulse. For filtering, we extend the size of the sliding window to cover more pixels information. The noisy pixel is replaced by output of weighted vector median filter implemented using the quaternion distance. More weight is assigned to those pixels belonging to the direction of minimum difference. Experimental results indicate the improved performance of the proposed filter in suppressing the impulse noise while retaining the original image details comparing against other well-known filters.

Impulse noise reduction in color images based on mean shift and channel suppression

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.

Hybrid Vector Filters based on Marginal Ordering for Impulsive Noise Suppression in Color Images

To attenuate impulsive noise in color images, a hybrid basic vector filter and its switching extensions are introduced in this paper. By utilizing reliable components provided by the marginal filter and retaining the inherent correlation between multi-channels, the new method selects the vector, which has minimal distance to the output of the marginal median filter. Based on this scheme, some well-known switching filters are easily modified to improve their noise suppression capability. The experiments demonstrate that the proposed filtering approach is more effective to suppress multichannel impulsive noise in color images, and its computation is more efficient than state-of-the-art basic vector filters. Moreover, extended experiments indicate that the noise suppression capability of several well-known switching vector filters can also be improved by adopting this basic scheme as an alternative approach at the replacement stage.

A New Optimum Method of Mean Difference Fuzzy Filter for Noise Reduction in Color Images

Image Reduction is one of the most important image processing tasks. In this article, a new Mean Difference fuzzy filter (MDFF) is presented for the removal of both Gaussian noise & impulse noise in color images. This new filter consists of two sub filters which are effectively employed to denoise both the Gaussian noise & impulse noise. This new fuzzy filter MDFF is applied only for the faulty pixel, without considering other original good pixels for which two thresholds are used to decide whether the pixel is corrupted or not. The first sub filter detects the noisy fixel along with the amount of noise with the help of membership functions using fuzzy logic. Fuzzy rules are constructed for fuzzy filters to realize the amount of noise [1]. Then the corrupted fixels are rectified with the help of noise free pixels. The second sub filter removes the noises in color images by using the relation between the color components of all corrupted pixels. This proposed fuzzy filter is tested with standard image under different noisy conditions using 3x3 windows. The proposed filter shows the better performance over all other existing standard some denoising filters in color images.

Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images

In this paper, a novel technique designed for the suppression of mixed Gaussian and impulsive noise in color images is proposed. The new denoising scheme is based on a weighted averaging of pixels contained in a filtering block. The main novelty of the proposed solution lies in the new definition of the similarity between the samples of the processing block and a small window centered at the block’s central pixel. Instead of direct comparison of pixels, a measure based on the similarity between a given pixel and the samples from the neighborhood of the central pixel is utilized. This measure is defined as the sum of distances in a given color space, between a pixel of the block and a certain number of most similar samples from the filtering window. The main advantage of the proposed scheme is that the new similarity measure is not influenced by the outliers injected into the image by the impulsive noise and the averaging process ensures the effectiveness of the new filter in the reduction of Gaussian noise. The experimental results prove that the novel filtering design is capable of suppressing mixed noise of high intensity and is competitive with respect to the state-of-the-art noise filtering methods.

Design of a new filtering for the noise removing in images by fuzzy logic

Removing noise from the color images is a very active research scope in image processing. In this paper, a new fuzzy based image filtering algorithm is proposed for reducing and removing impulse noise in color images. For dealing with the Impulse noise, an algorithm is developed to search for a set of uncorrupted pixels in the neighborhood of the pixel of interest and to compute the median of this set. A modified fuzzy filter consisting of two sub filters with novel membership functions is proposed to cancel out the impulse noise. The first sub filter detects the noisy pixel by utilizing three fuzzy membership functions, defined for this purpose. The corrupted pixels are then corrected using the median of the noise free pixels. The second sub filter makes use of the relation between different color components of a pixel to remove the residual noise in the color image. Simulation results shows that the proposed fuzzy filter effectively removes the additive noise by preserving fine details in the image.

Monochromatic-Based Method for Impulse Noise Detection and Suppression in Color Images

A new method to detect and reduce the impulse noise in color images is presented in this paper. The method consists of two stages: detection and filtering. Since each of the individual channels (components) of the color image can be considered as a monochrome image, both stages are applied to each channel separately, and then the individual results are combined into one output image. The corrupted pixels are detected in the first stage based on a proposed innovative switching technique. The noise-free pixels are copied to their corresponding locations in the output image. In the second stage, average filtering is applied only to those pixels which are determined to be noisy in the first stage, and only noise-free pixel values are involved in calculating this average. The size of the sliding window depends on the estimated noise density and is very small even for high noise densities. The proposed method is effective in noise reduction while preserving edge details and color chromaticity. Simulation results show that the proposed method outperforms all the tested existing state-of-the-art methods used in digital color image restoration in both standard objective measurements and perceived image quality.

Color impulsive noise removal based on quaternion representation and directional vector order-statistics

A new method for detecting and suppressing impulsive noise in color images is presented in this paper. The proposed method is a type of switching vector filters, where the impulse detection is based on the order-statistic information about the color samples in the horizontal, vertical, and diagonal directions. The new solution first uses quaternion-based representation of color differences and median deviation-based techniques to search for the edge direction with the maximum number of similar pixels, and then utilizes the samples aligning with this edge direction to judge whether the current pixel is noisy or not and control the switching between identity (no filtering) and vector median filtering actions. Extensive experimental comparisons exhibit the validity of the proposed approach by showing significant performance improvements over other well-known color image filtering techniques.

Impulsive Noise Removal in Color Image Using Interactive Evolutionary Computing

A new type of digital filter for removing impulsive noise in color images is proposed using interactive evolutionary computing. This filter is realized as a rule-based system containing switching median filters. This filter detects impulsive noise in color images with rules and applies switching median filters only at the noisy pixel. Interactive evolutionary computing (IEC) is adopted to optimize the filter parameters, considering the subjective assessment by human vision. In order to detect impulsive noise precisely, complicated rules with multiple parameters are required. Here, the relationship between color components and the degree of peculiarity of the pixel value are utilized in the rules. Usually, optimization of such a complicated rule-based system is difficult, but IEC enables such optimization easily. Moreover, human taste and subjective sense are highly considered in the filter performance. Computer simulations are shown for noisy images to verify its high performance.

Quaternion-based color image filtering for impulsive noise suppression

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.

Isolating impulsive noise pixels in color images by peer group techniques

A new method for removing impulsive noise in color images is presented. The fuzzy metric peer group concept is used to build novel switching vector filters. In the proposed filtering procedure, a set of noise-free pixels of high reliability is determined by applying a highly restrictive condition based on the peer group concept. Afterwards, an iterative detection process is used to refine the initial findings by detecting additional noise-free pixels. Finally, noisy pixels are filtered by maximizing the employed fuzzy distance criterion between the pixels inside the filter window. Comparisons are provided to show that our approach suppresses impulsive noise, while preserving image details. In addition, the method is analyzed in order to justify the necessity of the iterative process and demonstrate the computational efficiency of the proposed approach.

Iterative Self-adaptive Filtering Algorithm for Reducing Impulsive Noise in Color Images

Iterative Self-adaptive Filtering Algorithm for Reducing Impulsive Noise in Color Images

A neighborhood evaluated adaptive vector filter for suppression of impulse noise in color images

A new adaptive vector filter is proposed for impulse noise suppression and its relationship with the recent impulse reduction filters is investigated. The new filter detects outliers presented in the image through a novel neighborhood evaluation process, which significantly improves the accuracy of noise detection and detail preservation. The computational complexity of the new filter is very competitive. Its two parameters can be configured efficiently using online/offline optimization processes. Extensive simulations indicated that the new filter outperforms other prior-art methods in suppressing impulse noise in natural color images.

New Fuzzy Color Median Filter

Median filters are widely used in color image processing. In this paper a new fuzzy median filter is introduced, suitable for the removal of impulse noise in color images. In order to apply a median operator, some kind of ordering of vectors is required. Thus, a vector ordering scheme in the HSV color space using fuzzy if-then rules is described and then the median value operator is defined. The weighted fuzzy median is also studied. This filter performs significantly better in impulse noise removal, compared to other reported median filters. Experimental and comparative results of the application of the proposed filter and other similar reported filters, exhibit the superiority of the proposed method when applied to degraded color images.