Mixed Gaussian-impulse video noise removal via temporal-spatial decomposition

This paper studies the problem of the restoration of images corrupted by mixed Gaussian-impulse noise. In recent years, low-rank matrix reconstruction has become a research hotspot in many scientific and engineering domains such as machine learning, image processing, computer vision and bioinformatics, which mainly involves the problem of matrix completion and robust principal component analysis, namely recovering a low-rank matrix from an incomplete but accurate sampling subset of its entries and from an observed data matrix with an unknown fraction of its entries being arbitrarily corrupted, respectively. Inspired by these ideas, we consider the problem of recovering a low-rank matrix from an incomplete sampling subset of its entries with an unknown fraction of the samplings contaminated by arbitrary errors, which is defined as the problem of matrix completion from corrupted samplings and modeled as a convex optimization problem that minimizes a combination of the nuclear norm and the -norm in this paper. Meanwhile, we put forward a novel and effective algorithm called augmented Lagrange multipliers to exactly solve the problem. For mixed Gaussian-impulse noise removal, we regard it as the problem of matrix completion from corrupted samplings, and restore the noisy image following an impulse-detecting procedure. Compared with some existing methods for mixed noise removal, the recovery quality performance of our method is dominant if images possess low-rank features such as geometrically regular textures and similar structured contents; especially when the density of impulse noise is relatively high and the variance of Gaussian noise is small, our method can outperform the traditional methods significantly not only in the simultaneous removal of Gaussian noise and impulse noise, and the restoration ability for a low-rank image matrix, but also in the preservation of textures and details in the image.

The motivation of this paper is to introduce a novel framework for the restoration of images corrupted by mixed Gaussian-impulse noise. To this aim, first, an adaptive curvelet thresholding criterion is proposed which tries to adaptively remove the perturbations appeared during denoising process. Then, a new statistical regularization term, called joint adaptive statistical prior (JASP), is established which enforces both the local and nonlocal statistical consistencies, simultaneously, in a unified manner. Furthermore, a novel technique for mixed Gaussian plus impulse noise removal using JASP in a variational scheme is developed--we refer to it as De-JASP. To efficiently solve the above variational scheme, an efficient alternating minimization algorithm based on split Bregman iterative framework is developed. Extensive experimental results manifest the effectiveness of the proposed method comparing with the current state-of-the-art methods in mixed Gaussian-impulse noise removal.

Hyperspectral image denoising using constraint smooth rank approximation and weighted enhance 3DTV

We propose a nonlinear filter that is effective in removing both Gaussian and impulse noise. The proposed filter achieves an excellent performance in the restoration of images degraded by mixed noise, combining smoothing for Gaussian noise removal and inpainting for impulse noise removal on the basis of total variation minimization principle. We also propose how to determine smoothing parameter λ that is important for the quality of restored images, according to a surface model of λ with respect to σn and σs, standard deviations of Gaussian noise and original image respectively. In applying the proposed method to images degraded by various mixed noise, we verified that both PSNR and subjective image quality are significantly improved, compared with the conventional filters.

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.

Naturally available noises in the videos are complex but fortunately they can be broadly classified as Gaussian and Impulse noises. Most of the available models for noise removal emphasize on any one kind of noise removal thus an optimum model of mixed noise removal is still a challenge. This paper describes about removal of video flickering and artifacts due to sensor motion, unprofessional recording behaviors, device defects, poor lighting conditions and high dynamic exposure. The adaptive spatio-temporal filter gives excellent result for mixed (Gaussian and Impulse) noise removal. Dense optical flow is introduced to reduce the motion blur and enhance the video. The analysis of PSNR and SSIM values were compared with existed method like Non-local Means and BM3D approach and results are tabulated. The Histogram graph gives the better intensity distribution in frames thus the proposed method even works good for low illumination or night vision surveillance videos.

We have proposed fuzzy filters in order to remove additive nonimpulsive noise (e.g., Gaussian noise) while preserving signal details. In this paper, we propose a novel fuzzy filter for removing mixed noise (i.e., Gaussian noise and impulse noise are mixed). In order to remove mixed noise efficiently, we set fuzzy rules by using multiple difference values between arbitrary two pixels in a filter window. We show tuning result of the proposed fuzzy filter and present some simulation results.

In this article, we present a new algorithm for digital image processing noised by mixed Gaussian-impulse noise. Our mathematical model is based on the divide-conquer technique coupled with a reaction-diffusion system. We first decompose our image into low and high-frequency components by convolving each with a predefined convolutional filter. Further, we use a simple scheme of different weights to integrate and collect these processed sub-images into a filtered image. Finally, we apply our Reaction-Diffusion system to increase the contrast in the image. A number of experimental results are described to illustrate the performance of our algorithm and show that it is very effective in eliminating mixed Gaussian-impulse noise, increasing the contrast of the image and preserving the edges.

The project introduces a Fuzzy Based Weighted Vector Median Filter (FWMF) model to detect and eliminate the salt noise, pepper noise, Gaussian Mixed Impulsive noise. The mixed noises are applied to gray scale images and colour images. Through fuzzy logic methodology, the noises are detected and eliminated. Mixed impulsive and Gaussian noise reduction from digital color images is a challenging task because it is necessary to appropriately process both types of noise, that in turn need to be distinguished from the original image structures such as edges and details. Fuzzy theory is useful to build simple, efficient and effective solutions for this problem. In this paper, which proposes a fuzzy method to reduce Gaussian and impulsive noise from color images, uses one only filtering operation: a weighted Median. A fuzzy rule system is used to assign the weights so that both noise types are reduced and image structures are preserved. Experimental results should show the efficiency of the proposed fuzzy method, providing competitive results.

This paper proposes a novel non-parametric Bayesian framework for solving mixed noise removal problem. In order to removing unstable effects of outlier noise such as salt-and-pepper in the training data, we decompose the observed data model into three components terms of ideal data, Gaussian noise and sparse outlier. And the proposed model employs spike-slab sparse prior to find the sparser coefficients of desired data term and outlier noise. Note that the proposed non-parametric Bayesian model can infer the noise statistics from the training data and have been robust to the mixed noise without tuning of model parameters. Experimental results demonstrate our proposed algorithm performs well with mixed noise and achieves better performance over other state-of-the-art methods.

In recent years, the sparse coding-based techniques have been widely used for image denoising. However, most of the sparse coding-based mixed noise reduction methods fail to take full advantage of the geometric structure of data samples. In other words, they neglect the common information shared by the similar patches in sparse coding. To address this concern, in this paper, we propose a robust constrained sparse representation (RCSR) method to remove mixed noise. By using the center coefficient of similar patches as the guider which is approximated by the coefficient of query patch in sparse coding, the geometric structure of data can be well preserved. Moreover, different from most existing two-stage mixed noise reduction methods that use explicit detectors to restrain impulse noise, the proposed RCSR adaptively adjusts the contribution of each pixel in the loss function to eliminate the influences of outliers. Experiments on the reconstruction of synthetic data and the removal of mixed noise in real images demonstrate the effectiveness of our proposed method.

Hyperspectral images (HSIs) are frequently corrupted by various types of noise, such as Gaussian noise, impulse noise, stripes, and deadlines due to the atmospheric conditions or imperfect hyperspectral imaging sensors. These types of noise, which are also called mixed noise, severely degrade the HSI and limit the performance of post-processing operations, such as classification, unmixing, target recognition, and so on. The patch-based low-rank and sparse based approaches have shown their ability to remove these types of noise to some extent. In order to remove the mixed noise further, total variation (TV)-based methods are utilized to denoise HSI. In this paper, we propose a group low-rank and spatial-spectral TV (GLSSTV) to denoise HSI. Here, the advantage is twofold. First, group low-rank exploits the local similarity inside patches and non-local similarity between patches which brings extra structural information. Second, SSTV helps in removing Gaussian and sparse noise using the spatial and spectral smoothness of HSI. The extensive simulations show that GLSSTV is effective in removing mixed noise both quantitatively and qualitatively and it outperforms the state-of-the-art low-rank and TV-based methods.

Infrared images are susceptible to interference from a variety of factors during acquisition and transmission, resulting in the inclusion of mixed noise, which seriously affects the accuracy of subsequent vision tasks. To solve this problem, we designed a mixed noise removal algorithm for infrared images based on improved CycleGAN. First, we proposed a ResNet-E Block that incorporates the EMA (Efficient Multi-Scale Attention Module) and build a generator based on it using the skip-connection structure to improve the network’s ability to remove mixed noise of different strengths. Second, we added the PSNR (Peak Signal-to-Noise Ratio) as an extra calculation item of cycle consistency loss, so that the network can effectively retain the detailed information of infrared images while denoising. Finally, we conducted experimental validation on both synthetic noisy images and real noisy images, which proved that our algorithm can effectively remove the mixed noise in infrared images and the denoising effect is better than other similar methods.

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.

In this paper a FIR nonlinear fuzzy filter for image processing, which is most effective in removal of mixed noise, is proposed. In general itpsilas hard to distinguish noise and edges information. This ambiguity leads us to use fuzzy concepts. Fuzzy similarity is used here to suppress noise and preserve edges. Parameters of the membership function are optimized by genetic algorithm approach. Since our problem here is stochastic, traditional optimization algorithms are of no use anymore. Simulation consists of some combination of Gaussian and salt and pepper noises on different classes of images. Results are compared with traditional median and Wiener filters both from subjective and objective points of view.