An efficient algorithm for the removal of impulse noise from corrupted images

In this letter, we propose an efficient algorithm, which can successfully remove impulse noise from corrupted images while preserving image details. It is efficient, and requires no previous training. The algorithm consists of two steps: impulse noise detection and impulse noise cancellation. Extensive experimental results show that the proposed approach significantly outperforms many other well-known techniques for image noise removal.     

A new method to remove random-valued impulse noise in images

This paper presents a new method for detecting random-valued impulse noise (RVIN) in images. The proposed method is based on similar valued neighbor criterion and the detection of the noisy pixels are realized in maximum four phases. After the corrupted pixels detected in each phase, the median filtering is performed for only these pixels. As such, corrupted pixels are suppressed gradually at the end of the each phase. The performance of the proposed method is evaluated on different test images and compared with ten different comparison filters from the literature. It is shown from simulation results that proposed method provides a significant improvement over comparison filters.     

Similar neighbor criterion for impulse noise removal in images

In this paper, a new method is proposed for removing and restoring random-valued impulse noise in images. This approach is based on a similar neighbor criterion, in which any pixel to be considered as an original pixel it should have sufficient numbers of similar neighboring pixels in a set of filtering windows. Compared with other well known methods in the literature, this technique achieves superior performance in restoring heavily corrupted noisy images. Furthermore, it has low computational complexity, and equally effective in restoring corrupted color and gray-level images.     

A two-stage shearlet-based approach for the removal of random-valued impulse noise in images

In this paper, we introduce a novel two-stage denoising method for the removal of random-valued impulse noise (RVIN) in images. The first stage of our algorithm applies an impulse-noise detection routine that is a refinement of the HEIND algorithm and is very accurate in identifying the location of the noisy pixels. The second stage is an image inpainting routine that is designed to restore the missing information at those pixels that have been identified during the first stage. One of the novelties of our approach is that our inpainting routine takes advantage of the shearlet representation to efficiently recover the geometry of the original image. This method is particularly effective to eliminate jagged edges and other visual artifacts that frequently affect many RVIN denoising algorithms, especially at higher noise levels. We present extensive numerical demonstrations to show that our approach is very effective to remove random-valued impulse noise without any significant loss of fine-scale detail. Our algorithm compares very favourably against state-of-the-art methods in terms of both visual quality and quantitative measurements.     

The ANN based detector to remove random-valued impulse noise in images

This paper presents an artificial neural network (ANN) based method to detect random-valued impulse noise (RVIN) in images. The proposed method employs the ANN to decide whether a pixel is corrupted or not with RVIN. The inputs of the ANN are the rank ordered absolute differences (ROAD) and the rank-ordered logarithmic difference (ROLD) values. After the detection process is completed, the corrupted pixels are restored by the edge-preserving regularization (EPR) method which allows edges and noise-free pixels to be preserved. The performance of the proposed method is evaluated on different test images and compared with ten different comparison filters from the literature. Simulation results indicate that the proposed method provides significant improvement over comparison filters especially for high noise densities.     

An L0 regularized cartoon-texture decomposition model for restoring images corrupted by blur and impulse noise

In this paper, we address the image restoration case that includes both blurring and impulse noise. To recover an image with abundant features, we propose an L0 regularized cartoon-texture model for the simultaneous deblurring and impulse noise removal problem. We propose an L0 regularized framelet-based sparse representation and L0 regularized discrete cosine transform (DCT)-based sparse approximation to model the cartoon and texture of images, respectively. Unlike other cartoon-texture decomposition based-restoration approaches, our method does not depend on local features but globally controls the important non-zero components of the cartoon and texture in the framelet and DCT domain. Furthermore, we develop an alternating half-quadratic splitting method to solve the proposed L0 regularized cartoon-texture deblurring and impulse noise removal model (L0_RCTDINR) by introducing an alternating algorithm into the half-quadratic method. Experiments show the effectiveness of L0_RCTDINR on deblurring and impulse noise removal compared with existing state-of-the-art methods.     

Random-valued impulse noise removal using adaptive dual threshold median filter

Noise detection and its removal is very important in the image processing. Detection of noise is very crucial and significant in random valued impulse noise because it does not hamper the image pixels uniformly. This paper presents a novel and unique concept of adaptive dual threshold for the detection of random valued impulse noise along with simple median filter at noise removal stage. Simulation results shows that an efficient noise detection leads to a superior quality of de-noised image as compared to existing adaptive threshold based image de-noising techniques. Proposed threshold computation is based on averaging of pixel values of window which enhances the PSNR of our system as compared to existing median filter based image de-noising methods.     

An effective 2-stage method for removing impulse noise in images

In this paper, a robust 2-stage impulse noise removal system is proposed to remove impulse noise from extremely corrupted images. The contributions are in two-fold. First, a neuro-fuzzy based impulse noise detector (NFIDET) is introduced to identify the noisy pixels. NFIDET is a powerful noise detector that can handle image corruption even up to 90% with zero miss and false detection rate with a simple neuro-fuzzy structure. This is the best result among the other impulse noise detectors in the literature. Second, this paper presents a new approach for weight calculation of adaptive weighted mean filter by using robust statistical model. An adaptive robust weighted mean (ARWM) filter removes a detected noisy pixel by adaptively determining filtering window size and replacing a noisy pixel with the weighted mean of the noise-free pixels in its window. A Geman–McClure robust estimation function is used to estimate the weights of the pixels. Simulation results also show that the proposed robust filter substantially outperforms many other existing algorithms in terms of image restoration.     

Switching degenerate diffusion PDE filter based on impulselike probability for universal impulse noise removal

In this paper, a switching degenerate diffusion partial differential equation filter (SDDPDE) is developed by introducing the switching operators for reducing all kinds of impulse noise, and especially for images having a mixture of salt-and-pepper impulse noise and random-valued impulse noise which is a shortage for most of the existing filtering models. Our SDDPDE consists of the coarse and fine filtering stages. In the coarse filtering stages, the switching operator depends on a simple noise detector. In the fine filtering stages, we introduce the notion of impulselike probability, and the switching operator depends on both a simple noise detector and impulselike probability. Our SDDPDE will denoise noise pixels detected by the coarse detector while further modify the so-called noise-free pixels according to impulselike probability. The main advantages of our SDDPDE over published approaches are its simplicity and universality. In addition, we demonstrate the performance of our SDDPDE via application to three standard test images, corrupted by salt-and-pepper impulse noise, random-valued impulse noise and mixed impulse noise with high-noise levels, and the comparison with the other well-known filters. Experimental results show that our SDDPDE achieves high peak signal-to-noise ratio and better visual effect.     

A new efficient approach for the removal of impulse noise fromhighly corrupted images

A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in a sliding window. As part of this framework, several algorithms are examined, each of which is applicable to fixed and random-valued impulse noise models. First, a simple two-state approach is described in which the algorithm switches between the output of an identity filter and a rank-ordered mean (ROM) filter. The technique achieves an excellent tradeoff between noise suppression and detail preservation with little increase in computational complexity over the simple median filter. For a small additional cost in memory, this simple strategy is easily generalized into a multistate approach using weighted combinations of the identity and ROM filter in which the weighting coefficients can be optimized using image training data. Extensive simulations indicate that these methods perform significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques with as much as 40% impulse noise corruption. Moreover, the method can effectively restore images corrupted with Gaussian noise and mixed Gaussian and impulse noise. Finally, the method is shown to be extremely robust with respect to the training data and the percentage of impulse noise.     

Adaptive progressive filter to remove impulse noise in highly corrupted color images

In this paper, an adaptive progressive filtering (APF) technique with low computational complexity is proposed for removing impulse noise in highly corrupted color images. Color images that are corrupted with impulse noise are generally filtered by applying a vector-based approach. Vector-based methods tend to cluster the noise and receive a lower noise reduction performance when the noise ratio is high. To improve the performance, in the proposed technique, a new reliable estimation of impulse noise intensity and noise type is made initially, and then a progressive restoration mechanism is devised, using multi-pass non-linear operations with selected processing windows adapted to the estimation. The effect of impulse detection based on geometric characteristics and features of the corrupt pixel/pixel regions and the exact estimation of impulse noise intensity and type are used in the APF to efficiently support the progressive filtering mechanism. Through experiments conducted using a range of color images, the proposed filtering technique has demonstrated superior performance to that of well-known benchmark techniques, in terms of standard objective measurements, visual image quality, and the computational complexity.     

Salt and pepper noise removal in binary images using image block prior probabilities

During scanning and transmission, images can be corrupted by salt and pepper noise, which negatively affects the quality of subsequent graphic vectorization or text recognition. In this paper, we present a new algorithm for salt and pepper noise suppression in binary images. The algorithm consists of the computation of block prior probabilities from training noise-free images; noise level estimation; and the maximum a posteriori probability estimation of each image block. Our experiments show that the proposed method performs significantly better than the state of the art techniques.     

Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images

In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the second filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.     

An improved recursive and adaptive median filter for high density impulse noise

An improved recursive and adaptive median filter (RAMF) for the restoration of images corrupted with high density impulse noise is proposed in the present paper. Adaptive operation of the filter is justified with the variation in size of working window which is centered at noisy pixels. Based on the presence of noise-free pixel(s), the size of working window changes. The noisy pixels are filtered through the replacement of their values using both noise-free pixels of the current working window and previously processed noisy pixels of that window. These processed noisy pixels are obtained recursively. The combined effort thus provides an improved platform for filtering high density impulse noise of images. Experimental results with several real-time noisy images show that the proposed RAMF outperforms other state-of-the-art filters quantitatively in terms of peak signal to noise ratio (PSNR) and image enhancement factor (IEF). The superiority of the filter is also justified qualitatively through visual interpretation.     

A new impulse detector based on order statistics

The acquisition or transmission of digital images through sensors or communication channels is often interfered by impulse noise. It is very important to eliminate noise in the images before subsequent processing, such as image segmentation, object recognition, and edge detection. In this letter, a novel impulse noise-detection algorithm is presented, which can remove impulse noise from corrupted images successfully, and at the same time, without eliminating image details. The algorithm is based on the order statistics within a local window. Although our method is low in complexity when compared with some other complicated algorithms, experimental results show that it produces better restored images than many other existing techniques.     

一种用于抑制椒盐噪声的多窗口中值滤波器

该文提出了一种用于抑制椒盐噪声的多窗口中值滤波算法。算法在招待过程中根据具体情况采用不同大小的滤波窗口。仿真结果表明，与标准中值滤波算法相比，该方法不仅可以有效去除图像中的椒盐噪声，特别是在噪声密度非常大的情况下，表现了很好的性。     

基于像素梯度自适应迭代中值滤波器的图像脉冲噪声抑制算法

图像脉冲噪声移除是获得高质量图像的关键。本文通过热红外相机成像原理研究,提出了一种基于像素梯度自适应迭代中值滤波器的图像脉冲噪声抑制算法。首先,根据相机的调制传递函数计算获取原始图像的最大像素梯度,继而建立相应的像素梯度集合。然后,计算原始图像与对应像素梯度滤波图像的梯度权重均方根误差集合,并将该集合高斯分布的最大值对应的像素梯度确定为最佳像素梯度。最后,根据图像中脉冲噪声的密度和复杂度,确定所提滤波器的自适应窗口大小和迭代次数。大量实验结果表明,所提滤波器对移除8位、16位的单通道脉冲噪声图像展现出良好的鲁棒性。与其它先进方法相比,该方法可以实时移除真实热红外相机采集图像中低密度的随机值脉冲噪声和SAPN,并实现噪声抑制过程中99.5%以上的原始像素不会遭受破坏。除此之外,针对高密度SAPN抑制,该方法获得了具有竞争力的结果,与运行时间较快的滤波方法相比表现出较好的PSNR和SSIM,与PSNR和SSIM较优秀的去噪方法相比表现出较快的运行时间。对于极限SAPN（99%）破坏的图像,也能够恢复有意义的图像细节。     

Edge preserving adaptive anisotropic diffusion filter approach for the suppression of impulse noise in images

This paper proposes a new anisotropic diffusion approach to remove the impulse noise and retain the fine details. The proposed approach contains two stages, the first stage detects the impulse noise, and the second stage removes the noisy pixel and retains the fine details of the original image. The Laplacian operator is used to fine-tune the image quality of the restored image in the anisotropic diffusion filter. The proposed approach is tested with PSNR, IEF, correlation factor, and NSER for different test images and the results are compared against existing algorithms. The simulation results show that the proposed approach gives better results than the existing denoising algorithms.     

脉冲噪声图像的中-均值滤波算法

由于在图像信息的获取和传输过程中,图像常常受到不同程度的脉冲噪声污染。为了有效地去除高浓度脉冲噪声,提出了一种基于中－均值滤波器的噪声去除算法。该方法根据脉冲噪声特点,设定一个简单的噪声检测算子,根据噪声检测结果设定自适应滤波窗口,同时根据噪声密度选择中值和均值滤波器。为了更加有效地保留图像的原有信息,对非噪声点不做滤波处理。仿真结果表明,所提出的中－均值滤波方法不仅能有效地去除高浓度的脉冲噪声,而且能很好地保留图像的原有信息,并具有较短的滤波处理时间。     

A sparsity-ranking edge-preservation filter for removal of high-density impulse noises

In this study, a novel sparsity-ranking edge-preservation filter (SREPF) is proposed for removal of high-density impulse noise in images. Using the sparse matrix representation, the first stage of SREPF is not only to identify the noisy candidates but also to decide the processing order of them via a rank of noise-pixel sparsity in the working window. Then the second stage of SREPF utilizes a modified double Laplacian convolution to confirm the truly noisy pixels and yield a directional mean to recover them. This new approach has achieved more remarkable success rate of the edge detection than other edge-preservation methods especially in high noise ratio over 0.5. As a result, SREPF has significant improvements in terms of edge preservation and noise suppression exhibited by the peak signal-to-noise ratio (PSNR) and the structural similarity index metric (SSIM). Simulation results show that this method is capable of producing better performance compared to several representative filters.