Image denoising using boundary discriminated switching bilateral filter with highly corrupted universal noise

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

We observed that certain fundamental concerns commonly exist in some state-of-the-art switching-based median filters: (i) fixed thresholding for the pre-assumed noise density, (ii) the noise decision accuracy at high density impulse noise, and (iii) the filtering scheme adopted in response to pixel characteristic type identified. In this paper, we propose a novel noise adaptive soft-switching median (NASM) filter to effectively address the above-mentioned issues and achieve much improved filtering performance in terms of efficiency in removing impulse noise and robustness against noise density variations. Experimental results also reveal that the performance of our NASM filter is fairly close to that of ideal-switching median filter.

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.

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%.

A numbers of algorithms have been proposed for the removal of fixed valued impulse noise (salt and pepper) from highly corrupted gray scale and color images, but they failed to give better results at high noise densities. This paper is designed in accordance with the proposed algorithm to get better result at high noise density levels. The proposed algorithm works on two steps for de-noising the image first step is to detect that the pixel is noisy or not and the second step is the replacement of that noisy pixel. The proposed algorithm considers first order neighborhood pixels for detecting the noisy pixel and mean filter is considered. Color images are also de-noised by extracting the R, G and B pixels from noisy image and then they are de-noised separately and then merged together to again form the color image. Proposed algorithm is compared with all other standard and well known algorithms and found to have good noise removal capabilities at high densities. This algorithm shows better results than Median Filter (MF), Adaptive Median Filter (AMF), Progressive Switched Median Filter (PSMF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF), and Modified Non-Linear Filter (MNF). Different grayscale and color images are tested by using the algorithm and it gave better Peak Signal Noise Ratio (PSNR) and Image Enhancement Factor (IEF) at low, medium and high noise densities.

Median filter is a non-linear filter used in image processing for impulse noise removal. It finds its typical application in the situations where edges are to be preserved for higher level operations like segmentation, object recognition etc. This paper presents an accurate and efficient noise detection and filtering algorithm for impulse noise removal. The algorithm includes two stages: noise detection followed by noise filtering. The proposed algorithm replaces the noisy pixel by using median value when other pixel values, 0’s or 255’s are present in the selected window and when all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. Similarly algorithm checks for five different conditions to preserve image details, object boundary in high level of noise densities. This median filter was designed, simulated and synthesized on the Xilinx family of FPGAs (XC3S500E of Spartan-3E). The VHDL was used to design the above 2-D median filter using ISE (Xilinx) tool & tested & compared for different grayscale images.

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.

In this paper, we improve the trilateral filter for removing the mix of Gaussian and impulse noise. The new algorithm incorporates Rank-Ordered Absolute Differences (ROAD) Statistic for detecting outliers in gradient domain and intensity domain of image with impulse noise. A switching mechanism is adopted in gradient bilateral filter and intensity bilateral filter for smoothing the gradients and intensities of impulse noise samples and impulse noise-free samples with different parameters. By introduce the impulse detector in both gradient domain and intensity domain, the proposed algorithm has demonstrated superior performance in suppressing noise, which include Gaussian, impulse, and mixed noise. Compared to most other nonlinear filters, the proposed algorithm consistently yields good results in a sharply-bounded, gradient piecewise-linear approximation which provides stronger noise reduction and better edge-limited smoothing behavior.

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.

Bilateral filtering (BLF) and median filtering (MF) are key components in many applications. As the image resolution grows rapidly, implementation of efficient filtering is highly demanded. In this paper, we present a unified VLSI architecture that is able to compute both kinds of filters for 4k2k videos at 30fps. One feature of this design is that we leverage an emerging layer-based algorithm for both BLF and MF, which turns the non-linear BLF or MF into a set of linear filtering followed by output interpolation. It allows more accurate approximation than previous works. Moreover, to address the introduced design challenges of filter units and comparison units, we propose an architecture with moving sum method (MSM) of box filtering and a parallel component selector (PCS), respectively. The MSM requires only one line buffer, and the PCS architecture allows us to complete the comparison in one cycle. Furthermore, a shared pre-computation unit is also proposed to support both kinds of filters. As a result, our design costs only 6.33KB on-chip memory and 161.5K logic gates to support both BLF and MF. It is much efficient than existing architectures which support BLF only.

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level.

Elimination of impulse noise in image snap shots with side renovation is one of the complex duties in digital image processing. In this paper, the removal of random impulse noise is done in two important levels. In first level, the detection of the impulse noise is done on the premise of a double threshold selecting strategy after which in the another level, elimination of impulse noise is done by the usage of median filter and directional weighted median filter relying upon the noise map (Nmap) construction of corrupted pixels detected within the first level. The proposed method makes use of the statistical characteristics of noisy image graphs and the brink obtained is adaptable to one of a kind of snap shots and noise conditions. Comparative evaluation with different widespread de-noising techniques shows that the proposed method outperforms in terms of PSNR, SSIM, NMSE and Computation Time (CT) of the distinct trying out test images, with exclusive noise levels.

Digital signals are often contaminated by noise during signal acquisition and transmission for sewage sensing signal treatment such as aeration volume, oxygen content and water transparency etc. Sometimes, noise is a mixed one of gaussian noise and impulse noise. Unfortunately, existing denoising algorithms are often designed for removing single gaussian noise or impulse noise. In this paper, an efficient algorithm for mixed noise removal in signal is proposed, including space impulse noise removal and wavelet Gaussian noise removal. An impulse noise detection algorithm based on median filter is given to filter impulse signal, also a lifting wavelet was constructed by lifting original wavelet. The threshold based on lifting wavelet transform for signal was applied to denoising gaussian noise. Simulations are conducted on the presented algorithm, and the simulation result shows that this algorithm can remove mixed Gaussian and impulse noise in signal efficiently.

Most of the impulse denoisers are either median filter‐based or fuzzy filter‐based, which can only perform well in low noise conditions. This study presents an efficient convolutional neural network (CNN) with particle swarm optimisation (PSO) model for high‐density impulse noise removal. The proposed high‐density impulse noise detection and removal model mainly consists of two parts: the impulse noise removal and impulse noisy pixel detection for restoration. The authors’ model initially leverages the powerful ability of deep CNN architecture to separate noise from the noisy image, then adopts PSO to pinpoint the most optimised threshold values for detecting impulse noisy pixels. An ensemble of these algorithms is an intelligent and adaptive solution, producing a clean output while preserving significant pixel information. Targeting to solve high‐density impulse noise problems, the authors have trained their model with a massive collection of natural images and 14 standard testing images are used for validation purposes. In order to validate the robustness of the proposed method, different levels of high‐density impulse noise are considered. Based on the final denoised images, their model has proven its reliability, in terms of both visual quality and quantitative evaluation, on greyscale and colour images.

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.