Adaptive Switching Median Filter Research Articles

Extraction of significant features using GLDM for Covid-19 prediction

Although Covid-19 caused by the SARS-COV-2 virus, is a deadliest disease, many people experienced mild symptoms and were recovered soon. In this paper, coronavirus can be easily detected using CT scan images of affected patients. Initially, images are pre-processed by filters like Median filter and Noise adaptive fuzzy switching median filter, and then the quality measurements like MSE, and PSNR are calculated. After preprocessing, segmentation is done by K-means and Robust self sparse fuzzy clustering algorithm, and then the parameters like LMSE and NAE are calculated. Finally, to get optimum results, feature extraction using GLDM is performed which helps in identifying whether it's a normal lung disease like pneumonia or the patient is affected by covid.

Tropical algebra based adaptive filter for noise removal in digital image

The concept of the tropical algebra was first introduced to solve problems in mathematical economy such as optimization and approximation problems. In this paper, the concept of tropical algebra is used to build an image filtering algorithm. By using this concept, the lowest and highest pixel values are considered in determining the new pixel value. In addition, adaptive window will also be implemented to help the filtering process become more effective at high density noise. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to evaluate the output image quality produced by the filtering method. In this experiment, the performance of proposed method and existing methods such as switching median filter (SMF), adaptive fuzzy noise switching median filter (NAFSM), modified decision based on unsymmetric trimmed median filter (MDBUTMF), adaptive type-2 fuzzy filter (AT2FF) ), based on pixel density filters (BPDF), different applied median filters (DAMF), and tropical SVD filters (TSVD) will be compared. PSNR and SSIM results show that the proposed method outperforms most existing methods: SMF (27.13/0.7954), NAFSM (29.11/0.8459), MDBTUMF (29.18/0.8462), AT2FF (28.10/0.8159), BPDF (25.65/0.7545), DAMF (31.20/0.8833), TSVD (28.39/0.7906), and proposed (31.26/0.8827).

Noise Reduction on Bracketed Images for High Dynamic Range Imaging

The quality of a high dynamic range (HDR) image produced from bracketed images taken at different camera exposure times can be degraded by noise contained in bracketed images. In this paper, we propose a noise reduction method on bracketed images based on exposure time ratio. First, for each pixel pair of a same scene point lying on two different images, the ratio of their intensity values is compared with the ratio of exposure times of the images on which the pixels are lying. If the compared ratios are close, these two pixels are included in noise-free pixels set. The complement of noise-free pixels set is defined as noisy pixels set. Then, the intensity value of each pixel in noisy pixels set is corrected by its expected value computed from noise-free pixel of the same scene point lying on another image. Experimental results show that all the noisy intensity values can be correctly restored from noise-free pixels except the saturated pixels. Denoising results by PSNR show that the proposed method outperforms the other recent denoising methods such as based-on pixel density filter (BPDF), noise adaptive fuzzy switching median filter (NAFSMF), and adaptive Riesz mean filter (ARmF).

Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

Digital images may suffer from fixed value impulse noise due to several causes. The noise significantly degrades the quality of the image, which may affect the subsequence image processing. Therefore, a noise reduction technique is required to restore the image. In this paper, a new method, which is called improvement of quantized adaptive switching median filter (IQASMF), has been proposed to reduce the fixed value impulse noise from gray-scale digital images. The implementation of IQASMF has five processing blocks. The first processing block is the noise detection block, where the noise pixel candidates are detected based on the intensity value. Then estimation of the local noise density is done by the second processing block. Next, the third processing block filters the corrupted pixel candidates with filters of predefined size, depending on the local noise density. After that, the noise mask is updated in the fourth processing block. Finally, the fifth processing block processes the noise residuals from the third processing block by using a size adaptive filter. Experimental results from twenty standard gray-scale images of various sizes have shown that IQASMF has the ability to restore images for up to 99 % of the impulse noise corruption. As compared with the other five median filter-based methods, from the measures of mean squared error (MSE) and structural similarity index (SSIM), it is shown that the performance of IQASMF is equivalent to the performance of other methods at low and medium levels of corruption. However, at high corruption levels, IQASMF has demonstrated the best performance in terms of MSE and SSIM. The outputs from IQASMF also have the best visual appearance.

A new adaptive switching median filter for impulse noise reduction with pre-detection based on evidential reasoning

Image denoising is a fundamental problem in image processing. The switching filtering is a popular approach to reduce the impulse noise. It faces two challenges including the impulse noise detection and filter design. The traditional detection methods based on single criterion or multiple criteria encounter uncertainty problems and produce many miss-detections and false alarms, especially when the image is severely corrupted. In this paper, the uncertainties encountered in the impulse noise detection are addressed using the theory of belief functions, and a multi-criteria detection strategy for the impulse noise based on evidential reasoning is proposed. Based on the pre-detection, an adaptive median filter is designed, which adaptively determines the size of the filtering window according to the estimated global noise density and the degree of local corruption. Experimental results and related analyses show that our proposed image denoising method for the impulse noise has superior performance compared with several state-of-the-art denoising methods.

A new method based on pixel density in salt and pepper noise removal

In this paper, we deliver a new method to remove salt and pepper noise, which we refer to as based on pixel density filter (BPDF). The first step of the method is to determine whether or not a pixel is noisy, and then we decide on an adaptive window size that accepts the noisy pixel as the center. The most repetitive noiseless pixel value within the window is set as the new pixel value. By using 18 test images, we give the results of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image enhancement factor (IEF), standard median filter (SMF), adaptive median filter (AMF), adaptive fuzzy filter (AFM), progressive switching median filter (PSMF), decision-based algorithm (DBA), modified decision-based unsymmetrical trimmed median filter (MDBUTMF), noise adaptive fuzzy switching median filter (NAFSM), and BPDF. The results show that BPDF produces better results than the above-mentioned methods at low and medium noise density.

A new cluster based adaptive fuzzy switching median filter for impulse noise removal

A new clustering technique based on most allied directional neighbors is proposed to suppress low and high-density impulse noise from digital images. Most allied neighbors exhibit a vital role in estimation as well restoration of appropriate gray level value of corrupted pixels. In first phase, most allied directional neighbors, i.e., pixels directly attached to central pixel and the directional pixels (horizontal, vertical and two diagonal directions) next to attached pixels in the processing window are partitioned into two equal size clusters based on gradient values. Cluster with a minimum sum of gradient values (most similar neighbors) and the one with relatively large gradient values are passed to fuzzy inference system to infer the current pixel to be noisy-free, edge or a noisy. In second phase, a switching technique opts one of the three options depending upon fuzzy membership degrees and local information to restore the corrupted pixel value. A non-parametric approach based on local information for dynamic threshold setting using fuzzy logic makes the proposed filter computationally effective and adaptive to process a large number of images without user-defined parameters. The proposed algorithm is simple to implement and simulation results based on well know quantitative measures indicate the supremacy of the proposed filter for random-valued impulse noise as well as salt and peppers noise.

PSO Algorithm based Adaptive Median Filter for Noise Removal in Image Processing Application

A adaptive Switching median filter for salt and pepper noise removal based on genetic algorithm is presented. Proposed filter consist of two stages, a noise detector stage and a noise filtering stage. Particle swarm optimization seems to be effective for single objective problem. Noise Dictation stage works on it. In contrast to the standard median filter, the proposed algorithm generates the noise map of corrupted Image. Noise map gives information about the corrupted and non-corrupted pixels of Image. In filtering, filter calculates the median of uncorrupted neighbouring pixels and replaces the corrupted pixels. Extensive simulations are performed to validate the proposed filter. Simulated results show refinement both in Peak signal to noise ratio (PSNR) and Image Quality Index value (IQI). Experimental results shown that proposed method is more effective than existing methods.

Adaptive, Noise-Exclusive and Assessment-Based Fuzzy Switching Median Filter For High Intensity Impulse Noise

The article proposes an adaptive, noise-exclusive and assessment-based fuzzy switching median filter for high intensity impulse noise. The filter adaptively changes the size of sliding window in accordance with noise intensity and noise-exclusive operation is performed only on noise-free pixels. These steps thus preserve the image details effectively. Judicious assessment of the previously restored pixels is made in order to use them in replacing the noisy pixels and further improve the efficiency of filter. With these possibilities, fuzzy switching median filter-based approach is also incorporated for improving the performance. Experimental results justified the efficacy of the proposed fuzzy-based filtering method qualitatively and quantitatively for twelve different highly corrupted noisy images, and showed significant improvement to existing algorithms.

Two-dimensional wavelet algorithm for interferometric synthetic aperture radar phase filtering enhancement

Interferometric synthetic aperture radar (InSAR) coherence is valuable information used for quantifying the degree of accuracy of the interferometric phase (interferogram). We propose two new algorithms for InSAR filtering based on the coherence map. These are complementary algorithms for accurate InSAR phase filtering in the wavelet domain. The first algorithm is an enhanced version of the Lopez and Fabregas filter, called wavelet interferometric phase filter (WInPF), where the main improvement is in the noise mask growing step. The new algorithm overcomes the limited accuracy of noise mask computation used in the “Filtrage par Approche Multiechelle Modifie” (FAMM) algorithm by giving more accurate noise mask values with respect to the InSAR coherence information. The second approach proposed in this paper looks at reducing the impulse noise still tainting the SAR interferogram after operating the WInPF or the FAMM. Thus, we propose an improved version of the adaptive switching median filter dedicated to the case of the interferograms, which takes into account the corresponding coherence map values. The proposed approaches are validated on simulated interferograms and tested on real InSAR data acquired with the Radarsat-2 and Envisat satellites over Mahdia in Tunisia (North Africa) and Mount Etna in Italy (Europe).

Random-valued impulse noise removal by the adaptive switching median detectors and detail-preserving regularization

This paper firstly proposes an adaptive non-local switching median filter. Then, a two-phase scheme is presented to remove the random-valued impulse noise. In the first phase, the adaptive switching median filter or the adaptive non-local switching median filter is used to identify the pixels which are likely to be the noise candidates. In the second phase, only the noise candidates’ values are restored by a detail-preserving regularization method. Simulation results show that the proposed method is significantly superior to some of the state-of-the-art methods.

Adaptive Switching Median Filter Based on GA-BP Neural Network

For the poor performance of conventional filtering algorithms in removing salt and pepper noise from digital images under high noise density,an adaptive switching median filter algorithm based on BP neural network optimized by genetic algorithm (GA) is proposed to detect and remove salt and pepper noise from images. Firstly,the initial weights and thresholds of BP neural network are optimized by genetic algorithm.Then image pixels are devided into either signal or noise points by the trained network automatically. The detected noise points will be removed by adaptive switching median filter algorithm,but nothing to do with the signal points. Experiment results show that the proposed algorithm significantly outperforms the others and efficiently removes salt and pepper noise from digital images without distorting image details and textures .

Effective impulse noise reduction method based on local correlation

This paper proposes a local correlation-based switching median filter for impulse noise reduction. The proposed algorithm consists of two main steps, detection and correction. In the first step, noise detection is performed using a scalable detection mask and morphological operation. In the second step, a corrupted pixel is corrected using the local correlation between the uncorrupted pixels in the mask. The experimental results showed that the proposed method can reduce impulse noise significantly and preserve more edge information than the existing state-of-art methods. In addition, the proposed method outperforms the most effective two schemes, which are simple adaptive median filter (SAM) and iterative adaptive switching median filter (IASMF) by an average of 1·68 and 0·49 dB, respectively. Therefore, we believe that the proposed method can be a useful tool for impulse noise-related products such as television, infrared-based devices and digital cameras.

Predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector

A predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector (PASMF) is presented. The PASMF has a noise detector stage and a noise filtering stage. The noise detector implemented using feed forward neural network detects impulse noises in the corrupted image. The filter is a modified median filter, which removes detected impulse noise from the image. In contrast to the standard median filter, the PASMF computes the median value after predicting the appropriate values for neighboring corrupted pixels of the current central pixel in the filtering window. The results show that the PASMF gives better performance visually as well as in terms of different performance measures.

Enhanced Switching Median Filter for Denoising in 2D Patterned Textile Images

Wallpaper fabric, a part of patterned fabric, is a new decorative material covered with fabric or other natural fibre materials. Wallpaper fabrics are different from other indoor decorative materials and are designed for daily use with easy maintenance instructions. Because of their high demand, quality assurance or fault detection and recognition, in wallpaper fabrics is important. One step that is important during fault detection is the removal of noise. A common type of noise that affects patterned fabric images is the impulse noise. In this paper, a method that modifies a switching median filter is proposed. The algorithm contains two stages, namely, noise detection stage and noise filtering stage. During noise detection a modified adaptive switching median filter with three new thresholds are used to remove the impulse noise. During filtering stage, an adaptive switching median filter is used to determine the size of the sliding window. To detect noisy pixels which are close to its neighbours that are normally missed with the traditional threshold of switching median filter, a modified threshold that considers the ranking order of the pixels in the sliding window is used. Both the enhanced stages improve the noise removal process while maintaining image details. The proposed denoising model was evaluated using three performance metrics, namely, PSNR, FoM and execution time. The experimental results showed that the proposed model is efficient in removing impulse noise with low and high noise density while preserving important image and edge details.

Adaptive Switching Median Filter for Impulse Noise Removal Based on Support Vector Machines

This paper proposes a powerful SVM-ASM filter, the adaptive switching median(ASM) filter based on support vector machines(SVMs), to effectively reduce impulse noise in corrupted images while preserving image details and features. The proposed SVM-ASM filter is composed of two stages: SVM impulse detection and ASM filtering. SVM impulse detection determines whether the pixels are corrupted by noise or not according to an optimal discrimination function. ASM filtering implements the image filtering with a variable window size to effectively remove the noisy pixels determined by the SVM impulse detection. Experimental results show that the SVM-ASM filter performs significantly better than many other existing filters for denoising impulse noise even in highly corrupted images with regard to noise suppression and detail preservation. The SVM-ASM filter is also extremely robust with respect to various test images and various percentages of image noise.

변형된 적응 스위칭 메디안 필터를 이용한 임펄스 잡음제거에 관한 연구

As society has developed rapidly toward a highly advanced digital information age, a multimedia communication service for acquisition, transmission and storage of image data as well as voice has being commercialized. However, image data is always corrupted by various noises during image processing, so researches for removing noises have been continued until now. In this paper, in order to remove impulse noise we proposed modified adaptive switching median filter that consists of two stages: noise detection and noise removal. Proposed algorithm only processes noise pixels and these noise pixels are replaced by filter output, so proposed algorithm performs well not only removes noise but also preserves edge information. Also we compare existing methods using PSNR(peak signal to noise ratio) as the standard of judgement of improvement effect and choose conventional algorithms to compare with our proposed method.

A Mixed Nonlinear Filter for Image Restoration under AWGN and Impulse Noise Environment

Image denoising is a key issue in all image processing researches. Generally, the quality of an image could be corrupted by a lot of noise due to the undesired conditions of image acquisition phase or during the transmission. Many approaches to image restoration are aimed at removing either Gaussian or impulse noise. Nevertheless, it is possible to find them operating on the same image, which is called mixed noise and it produces a hard damage. In this paper, we proposed noise type classification method and a mixed nonlinear filter for mixed noise suppression. The proposed filtering scheme applies a modified adaptive switching median filter to impulse noise suppression and an efficient nonlinear filer was carried out to remove Gaussian noise. The simulation results based on Matlab show that the proposed method can remove mixed Gaussian and impulse noise efficiently and it can preserve the integrity of edge and keep the detailed information.

A novel fuzzy system for edge detection in noisy image using bacterial foraging

Bio-inspired edge detection using fuzzy logic has achieved great attention in the recent years. The bacterial foraging (BF) algorithm, introduced in Passino (IEEE Control Syst Mag 22(3):52---67, 2002) is one of the powerful bio-inspired optimization algorithms. It attempts to imitate a single bacterium or groups of E. Coli bacteria. In BF algorithm, a set of bacteria forages towards a nutrient rich medium to get more nutrients. A new edge detection technique is proposed to deal with the noisy image using fuzzy derivative and bacterial foraging algorithm. The bacteria detect edge pixels as well as noisy pixels in its path during the foraging. The new fuzzy inference rules are devised and the direction of movement of each bacterium is found using these rules. During the foraging if a bacterium encounters a noisy pixel, it first removes the noisy pixel using an adaptive fuzzy switching median filter in Toh and Isa (IEEE Signal Process Lett 17(3):281---284, 2010). If the bacterium does not encounter any noisy pixel then it searches only the edge pixel in the image and draws the edge map. This approach can detect the edges in an image in the presence of impulse noise up to 30%.

Cluster-based adaptive fuzzy switching median filter for universal impulse noise reduction

In this paper, we present a novel method for the removal of impulse noise from digital images. The proposed filter, called the Cluster-based Adaptive Fuzzy Switching Median (CAFSM), is composed of a cascaded easyto- implement impulse detector and a detail preserving noise filter. Initially, the impulse detector classifies any possible impulsive noise pixels. Subsequently, the filtering phase replaces the detected noise pixels. In addition, the filtering phase employs fuzzy reasoning to deal with uncertainties present in local information. Contrary to many existing filters that only focus on a particular impulse noise model, the CAFSM filter is capable of filtering all kinds of impulse noise -the random-valued and/or fixed-valued impulse noise models. Extensive simulations conducted on 100 monochrome images under a wide range of noise densities show that the CAFSM filter substantially outperforms other state-of-the-art impulse noise filters. Furthermore, the relatively fast processing time suggests the CAFSM filter’s applicability in consumer electronic products such as digital cameras <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .