Pepper Impulse Noise Research Articles

Adaptive switching interpolation filter for restoring impulse corrupted digital images

The study introduces an adaptive switching interpolation filter (ASIF) for restoring images contaminated with salt & pepper impulse noise. The new method restores noisy pixels by applying a new interpolation scheme that combines Shepard's inverse distance weighting and Simpson's natural neighbour interpolation techniques. The new interpolation scheme is designed based on the percentage of the overlapping area and the distance between the sites created by noisy pixels with the sites of uncorrupted pixels in the Voronoi tessellation. Since natural neighbour-based techniques do not support extrapolation, the proposed algorithm performs Shepard's inverse distance weighting-based restoration when the noisy pixels fall outside the convex hull formed by uncorrupted pixels. As the proposed method combines the advantages of Simpson's natural neighbour and Shepard's inverse distance weighting interpolation methods, it better preserves the natural intensity variations in images and provides smoother approximation than other methods used in the comparative study. Visual and quantitative experimental analysis performed on various images with peak signal to noise ratio, mean structural similarity index measure, image enhancement factor, and feature-similarity index measure demarcates the improved capability of ASIF over other comparative filters.

Implementation of Novel Image Restoration Technique from High Density Impulse Noise through Adaptive Max-Min Filter

Noise is ardent factor that reduces the quality of any system, disturbing the corresponding output. This is very noticeable especially in image processing techniques, wherein the image output is distorted to a very large extent. To reduce the effect of noise and to improve the quality of any image, it is mandatory to completely remove high density salt-and-pepper impulse noise. The salt-and –pepper impulse noise is a major hindrance that has to be eliminated by restoring the image using a specialized method that applies to all noise pixels. Involving edge protection and noise control, significant improves image quality compared to those restored by using just a nonlinear filter. The significant feature of the proposed idea is that removal of salt-and-pepper-noise as high as 95%.is obtained.

Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations

Images may be corrupted by salt and pepper impulse noise during image acquisitions or transmissions. Although promising denoising performances have been recently obtained with sparse representations, how to restore high-quality images remains challenging and open. In this work, image sparsity is enhanced with a fast multiclass dictionary learning, and then both the sparsity regularization and robust data fidelity are formulated as minimizations of L0-L0 norms for salt and pepper impulse noise removal. Additionally, a numerical algorithm of modified alternating direction minimization is derived to solve the proposed denoising model. Experimental results demonstrate that the proposed method outperforms the compared state-of-the-art ones on preserving image details and achieving higher objective evaluation criteria.

High Density Salt and Pepper Noise Removal in Images Using Adapted Decision Based Unsymmetrical Trimmed Mean Filter Cascaded with Gaussian filter

An adapted decision based unsymmetrical trimmed mean filter cascaded with Gaussian filter (ADCG) algorithm for the retrieval of gray scaled image which is induced by a very high density Salt and Pepper (impulse) noise is proposed and tested in this paper. The proposed calculation supplants the noisy pixel by trimmed mean quality when other pixel values, 0's and 255's are available in the selected window and when all the pixel qualities are 0's and 255's then the noisy pixel is supplanted by mean estimation of the considerable number of components present in the selected window. This proposed methodology shows preferred results over the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), Progressive Switched Median Filter (PSMF) and Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). The proposed methodology is tried against distinctive pictures and it gives better PSNR that is Peak Signal-to-Noise Ratio.

A robust medical image watermarking against salt and pepper noise for brain MRI images

The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients' privacy be protected. During the transmission of medical images between hospitals or specialists through the network, the main priority is to protect a patient's documents against any act of tampering by unauthorised individuals. Because of this, there is a need for medical image authentication scheme to enable proper diagnosis on patient. In addition, medical images are also susceptible to salt and pepper impulse noise through the transmission in communication channels. This noise may also be intentionally used by the invaders to corrupt the embedded watermarks inside the medical images. A common drawback of existing watermarking methods is their weakness against salt and pepper noise. The research carried out in this work addresses the issue of designing a new watermarking method that can withstand high density of salt and pepper noise for brain MRI images. For this purpose, combination of a spatial domain watermarking method, channel coding and noise filtering schemes are used. The region of non-interest (RONI) of MRI images from five different databases are used as embedding area and electronic patient record (EPR) is considered as embedded data. The quality of watermarked image is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the accuracy of the extracted watermark is assessed in terms of Bit Error Rate (BER).

Probabilistic decision based filter to remove impulse noise using patch else trimmed median

A new probabilistic decision based filter (PDBF) is presented to remove salt and pepper impulse noise in highly corrupted images. The filter employs two types of estimation techniques for denoising namely trimmed median (TM) and patch else trimmed median (PETM) which is our main contribution in this paper. Depending upon the estimated noise density, the filter utilizes either TM or PETM and hence enhanced outcome of denoising. Simulation results prove that the PDBF has outperformed recently proposed state-of-the-art filters in terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM), image enhancement factor (IEF), mean absolute error (MAE) and visual representation at the noise densities (ND) as high as 95%.

Switching Directional Median Filter Specially Designed for Impulse Noise and Its Comparison with other Median Filters

Now a days, median filters are used to de-noise corrupted digital images. A variety of noises occur in the channel but the major occurrence is of impulse noise. The hardship in de-noising is that the image gets corrupted with a high density of impulse noise. Median filters are modified to provide better results on corrupted images. A new median filter based on detection and de-noising technique is proposed to reduce the frequent occurrence of salt & pepper impulse noise. The proposed method is very effective in de-noising the images corrupted with high density salt & pepper noise. Keywords: SMF (standard median filter), SWMF (switching weighted median filter), DWM (directional weighted median filter), DWMSP (directional weighted median filter for salt & pepper).

An efficient three-stage approach for removing salt & pepper noise from digital images

In this paper, an efficient three-stage scheme for the removal of salt & pepper noise based on an efficient impulse detector, an adaptive mean-median filter and the total variation inpainting method was proposed. This approach removes salt & pepper noise by detecting, estimating and modifying noisy pixels in an image. If a pixel is classified as noise, its intensity is first estimated by a mean-median filter; and then an accurate estimation is obtained through the use of the total variation inpainting method. Conversely, the pixel value is kept unchanged when it is classified as noise-free, yielding the quality of the restored image being well maintained. Experimental results demonstrate that the proposed three-stage approach can not only remove salt & pepper impulse noise efficiently, but also preserve the detailed information of an image well.

Image De-noising Using Multilevel Filtering Approach

Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. Salt & pepper (Impulse) noise and the additive white Gaussian noise and blurredness are the types of noise that occur during transmission and capturing. To remove these types of noise we have many filters like mean filter, median filter, inverse filter, wiener filter. No single one filter can remove both types of noise. A new approach for de-noising based on multilevel filter is proposed in this paper. The numerical computation has been done using MATLAB 7.8.0. Keywords: Peak Signal to noise ratio, Image denoising, Image restoration, Filter.

Histogram-Based Gaussian Noise Removal

In modern science and technology, as the digital image processing gets more and more importance, the process of image quality enhancement and image restoration becomes a matter of concern for the researchers. In an image, denoising complexity increases from salt and pepper impulse noise to random-valued impulse noise, through to Gaussian noise. As salt and pepper noise occupies either high (255) or low (0) values and also as it is not distributed uniformly, hence, with the help of correct neighbouring pixels and known value of noise, the corrupted pixels can be easily restored. In random-valued noise, the value of noise is unknown, but we can take advantage of the non-uniform noise distribution, and using non-corrupted neighbouring pixels values, we can restore the corrupted pixels. However, as Gaussian noise is a uniform noise, hence, not only the value of noise is unknown, but also it is not possible to get non-corrupted neighbouring pixels. In Gaussian noise, restoring pixels using the corrupted neighbouring pixels is a highly tedious job. In this paper, an efficient algorithm for the removal of Gaussian noise is proposed. Experimental results show that proposed algorithm produces good results up to 50% of noise level, the value normally real-time images were corrupted.

Salt and Pepper Noise Removal with Noise Detection and a Patch-Based Sparse Representation

Images may be corrupted by salt and pepper impulse noise due to noisy sensors or channel transmission errors. A denoising method by detecting noise candidates and enforcing image sparsity with a patch-based sparse representation is proposed. First, noise candidates are detected and an initial guide image is obtained via an adaptive median filtering; second, a patch-based sparse representation is learnt from this guide image; third, a weightedl1-l1regularization method is proposed to penalize the noise candidates heavier than the rest of pixels. An alternating direction minimization algorithm is derived to solve the regularization model. Experiments are conducted for 30%∼90% impulse noise levels, and the simulation results demonstrate that the proposed method outperforms total variation and Wavelet in terms of preserving edges and structural similarity to the noise-free images.

A Novel Approach for Salt and Pepper Noise Removal using ROR and Contourlet Transform

ROR and Contourlet transform is proposed to restore images corrupted by salt and pepper impulse noise. The operation is carried out in two stages, i.e., detection followed by filtering. For detection first, we propose the robust outlyingness ratio (ROR) for measuring how impulse like each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, proposed algorithm using contourlet transform (CT). Simulation results demonstrate that the proposed algorithm is better than traditional filters and is particularly effective for the cases where the images are very highly corrupted.

Machine learning and directional switching median-based filter for highly corrupted images

In this paper, two-stage machine learning-based noise detection scheme has been proposed for identification of salt-and- pepper impulse noise which gives excellent detection results for highly corrupted images. In the first stage, a window of size $$3\times 3$$ is taken from image and some other features of this window are used as input to neural network. This scheme has distinction of having very low missed detection (MD) and false positives rates. In the second stage, decision tree-based algorithm (J48) is applied on some well-known statistical parameters to generate rules for noise detection. These noise detection methods give promising results for identification of noise from highly corrupted images. A modified version of switching median filter (directional weighted switching median filter) is proposed for noise removal. Performance of noise detector is measured using MD and false alarm FA. Filtering results are compared with state-of-the-art noise removal techniques in terms of peak signal-to-noise ratio and structural similarity index measure. Extensive experiments are performed to show that the proposed technique gives better results than state-of-the-art noise detection and filtering methods.

A Study on Image Restoration Algorithm in Random-Valued Impulse Noise Environment

Digital images are often corrupted by impulse noise, and it is very important to remove random-valued impulse noise. Cleaning such noise is far more difficult than cleaning salt and pepper impulse noise. In this paper, we proposed an efficient way to remove random-valued impulse noise from digital images. This novel method comprises two stages. The first stage is to detect the random-valued impulse noise in the image and the pixels are roughly divided into two classes, which are "noise-free pixel" and "noise pixel". Then, the second stage is to eliminate the random-valued impulse noise from the image. In this stage, only the "noise pixels" are processed. The "noise-free pixels" are copied directly to the output image. Simulation results indicated that our method provides a significant improvement over many other existing algorithms.

A New Framework of Removing Salt and Pepper Impulse Noise for the Noisy Image Including Many Noise-Free White and Black Pixels

In this paper, we propose a new framework of removing salt and pepper impulse noise. In our proposed framework, the most important point is that the number of noise-free white and black pixels in a noisy image can be determined by using the noise rates estimated by Fuzzy Impulse Noise Detection and Reduction Method (FINDRM) and Efficient Detail-Preserving Approach (EDPA). For the noisy image includes many noise-free white and black pixels, the detected noisy pixel from the FINDRM is re-checked by using the alpha-trimmed mean. Finally, the impulse noise filtering phase of the FINDRM is used to restore the image. Simulation results show that for the noisy image including many noise-free white and black pixels, the proposed framework can decrease the False Hit Rate (FHR) efficiently compared with the FINDRM. Therefore, the proposed framework can be used more widely than the FINDRM.