Noise adaptive soft-switching median filter for image denoising

The paper compares the performance of various filters of different sizes on impulse noise in spatial domain. In this paper median and adaptive median filters are used from non-linear category. For a low density impulse noise, smaller size median filter is required while for a high density impulse noise, either a larger size median filter is required or a series of smaller size median filter is required. The circular averaging filter also performs well in this case but at the expense of more blurring. Moreover alpha trimmed mean filter works well with a moderate density impulse noise. Finally the adaptive median filter works best on impulse noise. The adaptive median filter can recover the image even if the image is not visible in case of very high density impulse noise. The paper demonstrates some novel procedures involving a series of filters to handle high density impulse noise.

Noise Suppression from images is one of the most important concerns in digital image processing. Impulsive noise may occur during image acquisition, transmission or storage Noise should be removed in such a way that important information of image should be preserved. We can use so many algorithms for getting the original image, by removing salt and pepper noise from the corrupted images. In this paper an algorithm is proposed for the restoration of gray scale images that are highly corrupted by impulse noise (salt and pepper noise). There are two phases in the proposed algorithm. First phase detects whether the processing pixel is corrupted or not. In the Second phase it recreates the corrupted pixel by means of the proposed algorithm. This algorithm shows better results than the Standard Median Filter (MF), Center Weighed Median Filter(CWM), Adaptive Median Filter(AMF), Adaptive Center Weighed Median Filter (ACWM), Decision Based Algorithm (DBA) and Modified Decision Based Unsymmetrical Trimmed Median Filter Algorithm(MDBUTMF). Obtained results with different grayscale images shows that proposed algorithm gives better Peak Signal-to-Noise Ratio (PSNR) and less Computational time and works well in removing salt and pepper noise at low, medium and high noise densities.

A new method based on adaptive median filtering is proposed in this paper to remove high density impulse noise. This method replaces central noisy pixel having gray scale value 0 or 255 by median value of 3×3 window. Algorithm is proposed to replace the noisy neighbor pixels in the window. The clean pixels are then used to calculate the median value, which then replaces noisy central pixel. Proposed method has been tested on images which were contaminated by impulse noise of different densities, and results are compared with existing simple median filter with window size of 3 × 3, 5 × 5, and adaptive median filter. Results shows that Proposed method gives better Mean Square Error (MSE), Universal Quality Index (UQI), and Peak Signal to Noise Ratio (PSNR), when compared with the simple and adaptive median filter.

Impulsive noise usually has adverse effect on image processing. It has been proved that adaptive median filter is effective to impulsive noise removal. However, high percentage impulsive noise is still a challenge to adaptive median filter. Thus, a sequential median filter is proposed in this paper. Adaptive median filter and median filter are combined in sequence to handle high percentage impulsive noise. Moreover, variable window sizes are employed in the sequential median filter. Experimental results indicate that the sequential median filter is effective for high percentage impulsive noise removal.

As the most popular nonlinear denoise technique, the median filter has attracted significant attention in recent years. In this paper, a novel adaptive median filter is presented to remove random-valued impulse noise in images, named Adaptive Partition-Cluster-Based Median (APCM) Filter. Based on the partition cluster idea, the noise detector classifies pixels into different groups and identifies the noisy pixels in different regions adaptively without iterations. According to the results of noise detection, an improved adaptive decision-based filter is presented to restore the pixels which are corrupted by random-valued impulse noise. The proposed filter technique is open to any impulse noise. Extensive simulation results demonstrate that the proposed method substantially outperforms other state-of-the-arts impulse noise filter techniques both visually and in terms of objective quality measures. Furthermore, the proposed method is much friendly to the hardware parallel implementation of image processing because of its low computation complexity and simple realizable structure.

There are many techniques for face recognition which compare the desired face image with a set of faces images stored in a database. Most of these techniques fail if faces images are exposed to high-density noise. Therefore, it is necessary to find a robust method to recognize the corrupted face image with a high density noise. In this work, face recognition algorithm was suggested by using the combination of de-noising filter and PCA. Many studies have shown that PCA has ability to solve the problem of noisy images and dimensionality reduction. However, in cases where faces images are exposed to high noise, the work of PCA in removing noise is useless, therefore adding a strong filter will help to improve the performance of recognizing faces in the case of existing high-density noise in faces images. In this paper, Median filter, Hybrid Median Filter, Adaptive Median filter, and Adaptive Weighted Mean Filter were used to remove the noise from the faces images, and they were compared in order to use the best of these filters as a pre-processing step before the face recognition process. Experimental results showed that the Adaptive Weighted Mean Filter gave better results compared with the other filters. Thus, the performance of face recognition process was improved under high-density noise using the Adaptive Weighted Mean Filter and Principal Component Analysis. For the corrupted images by 90 % noise density, Recognition rate by using Median Filter reached 0% and 33% by using Hybrid Median Filter. While Recognition rate by using the Adaptive Median Filter and Adaptive Weighted Mean Filter reached 100%.

There has been extensive research on removing universal noise from corrupted images using various kinds of non-linear filters. Median and bilateral filters are basic nonlinear filters which have been modified over the years to improve the quality of images. Median filters are good at removing Impulse noise, while these filters fail terribly in removing Gaussian noise. For Gaussian noise removal bilateral filter is preferred and successful over the years. But bilateral filter is not effective in removing impulse noise. Switching Bilateral Filter (SBF) works good for removing universal noise which involves noise detection followed by filtering. Detection plays vital role in image denoising of highly corrupted images. Boundary Discriminated Noise Detection (BDND) algorithm is very efficient in detection of noise even in highly corrupted images. Boundary Discriminated Switching Bilateral Filter (BDSBF) is proposed which employs BDND detection followed by SBF for better accuracy in noise detection even at higher noise conditions. Proposed filter also changes the size of the window based on the noise density computed from noise statistics after the BDND detection. Proposed filter shows superior results over SBF under high noise densities and high noise standard deviations.

This paper presents a novel significance driven inverse distance weighted (SDIDW) filter for the impulsive noise removal in the X-ray images. The proposed SDIDW filter restores the noisy pixel using minimum number of nearest noise-free pixels to achieve good estimation while exhibiting low computational complexity. In the proposed filter, higher priority (weight) is given to nearest pixels compared to distant pixels and only sufficient nearest noise free pixels are determined to estimate the value of noisy pixel. A high level analysis of the computation complexity at varying noise density is done which shows that proposed SDIDW filter provides significant reduction in computation complexity over the adaptive median filters. Finally, the performance of the proposed filter is evaluated and compared over the state-of-the-art impulse noise removal techniques for varying noise density (wide range 10–90% and very high noise density range 91–99%). The experimental results on medical images demonstrate significant improvement in filtered images quality by the proposed filter over the state-of-the-art filters at each sample of noise density with small computational complexity.

Multiclass SVM based adaptive filter for removal of high density impulse noise from color images

Laser image often mixes with salt and pepper noise when obtained and transmitted by image sensor. The salt and pepper noise not only makes the quality of laser image deteriorated but also causes the detail feature of laser image flooded which contains very important structure information. To remove salt and pepper noise effectively meanwhile ensure image details clear and completed, a Novel Adaptive Switching Median (NASM) filter based on local salt and pepper noise density is presented in this paper. Pixel points are divided into salt and pepper noise points and signal points according to two level detection mechanisms firstly, then, local salt and pepper noise density is introduced here to determined filter window of every noise point, only noise points are filtered by different size window adaptively whereas signal points are kept unprocessed finally. Through analyzing of experimental results, it shows that NASM filter can remove salt and pepper noise effectively meanwhile protect image details well thus getting better filtering performance compared with Traditional Median (TM) filter and other improved Switching Median (SM) filters for laser image.

Multi-frequency phase sensitive demodulation plays an important role in electrical impedance tomography (EIT). The phase sensitive detector is an effective tool for discriminating the amplitude and phase of each frequency component signal. It can be considered as the optimal linear filter in case of Gaussian-type noise. In real applications, however, the noise has also rather distinctive impulsive characteristics. It is a challenging task to remove the impulsive noise effectively in multi-frequency phase sensitive demodulation. In this paper, an approach for impulsive noise removal is presented in multi-frequency phase sensitive demodulation, which takes advantage of the periodicity of a multi-frequency signal and the significant properties of the median filter. The current work focuses on the use of two-dimensional (2D) median filter to handle the impulsive noise that existed in the measured signal, i.e. a 2D median filter is employed as the pre-stage signal conditioning module prior to the phase sensitive detector. The main advantages of the proposed method are that: 1) increasing the number of samples within the median filter window, which benefits the consecutive impulsive noise removal, and 2) no additional storage and computational resources are required for hardware system. Two classical methods are selected as comparisons in experiment. Numerical simulations and experimental results demonstrate that the proposed method can effectively eliminate the influence of impulsive noise on multi-frequency phase sensitive demodulation, and is able to achieve outstanding signal-to-noise ratio (SNR) performance.

Impulse noise is one of the most important factors in degrading of image quality. In this paper, a novel technique is presented for detecting and removing of impulse noise, while the significant information of image, such as edges and texture, are remind untouched. The proposed algorithm use the weighted window with variable sizes and apply median filtering on them. Simulation results, with various images and noise intensities, show that the proposed algorithm has better performance compared with state of the art methods and increases the PSNR value (of the reconstructed image) up to 4dBs.

In the world of image processing domain, removal of high density impulse noise is always a prominent area for research. In this paper, an efficient Modified Decision based unsymmetric trimmed median filter algorithms for the removal of impulse noise has been proposed with color images rather than gray scale images by separation red- green- blue plane of color image. The performance of the system is analyzed in terms of Mean square error (MSE), Peak signal to noise ratio (PSNR) image enhancement factor (IEF) and time required for executing the algorithms for different noise densities. Simulation results shows that proposed algorithm outperforms the existing algorithms even at high noise densities for color images. Many experiments are conducted to validate efficiency of the proposed algorithm.

Removal of impulse noise from color images is a stringent job in the arena of image processing. Impulse noise is fundamental of two types: Salt and pepper noise (SAPN) and Random valued impulse noise (RVIN). The key challenge in impulse noise removal from color images lies in tackling out the randomness in the noise pattern and in handling multiple color channels efficiently. Over the years, several filters have been designed to remove impulse noise from color images, but still, the researchers face a stringent challenge in designing a filter effective at high noise densities. In this study, a combination of K-means clustering-based detection followed by a minimum distance-based approach for removal is taken for high-density impulse noise removal from color images. In the detection phase, K-means clustering is applied on combined data consisting of elements from designated 5 × 5 windows of all the planes from RGB color images to segregate noisy and non-noisy elements. In the removal phase, noisy pixels are replaced by taking the average of medians of all non-noisy pixels and non-noisy pixels under 7 × 7 windows residing at least Manhattan distance from the inspected noisy pixel. Performance of the proposed method is evaluated and compared up against the latest filters, on the basis of well-known metrices, such as Peak signal to noise ratio (PSNR) and Structural similarity index measurement (SSIM). Based on these comparisons, the proposed filter is found superior than the compared filters in removing impulse noise at high noise densities.

An adaptive median filter with a circular kernel, named as circular adaptive median filter (CAMF) is proposed in this article, for denoising of magnetic resonance imaging (MRI) images corrupted by salt and pepper noise of varying noise densities. An adaptive operation is incorporated in the proposed filter by varying the size of the circular kernel according to the requirement. The effectiveness of the CAMF is compared with six other competitive networks, i.e., conic adaptive median filter (CoAMF), decision based filter (DBF), modified switching median filter (MSWM), recursive adaptive modified filter (RAMF), plus adaptive median filter (PAMF), and cross adaptive median filter (CrAMF). The performance of all the models is analyzed using peak signal to noise ratio (PSNR) and computational time. Moreover, a non-parametric statistical test is conducted to illustrate the pair wise comparison of other filter with the proposed one. It is observed that the proposed approach has demonstrated superior performance with respect to the two performance measures.