Feature Edge-Detail Preservation of Random-Valued Impulse Noise in Images (original) (raw)
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A novel edge preserving filter for impulse noise removal
… , Signal Processing and …, 2011
This paper proposes an edge preserving filter for removal of impulse noise. Digital images received from various sources are often degraded due to impulse noise and thus become unsuitable for further processing. To overcome this degradation removal of impulse noise is very important. In this paper an effective and efficient method of impulse noise removal is proposed which not only removes noise but also preserves edges. The algorithm first finds noisy, noise free and edge pixels. Then it replaces the noisy pixel with a pixel from its neighbourhood which is nearest to the adaptive median of the noisy pixel, this removes the noise as well as preserves edges and fine image details.
International Journal of Recent Technology and Engineering
Denoising an image is a significant problem in the processing of digital images. Any impulse noise damages the image and the aim of denoising is to remove noise and restore the high-quality image as much as possible. This paper aims to develop a method to discriminate between corrupted and uncorrupted pixels and develop a novel filter to denoise the image. It is also necessary to consider images with different level of noise of various applications to develop an optimal system to remove the noise for further processing. In this paper different filtering techniques such as Median-Filter (MF), Weighted -Median-Filter (WMF),Centre-Weighted Median filter (CWMF), and adaptive centre weighted median filter (ACWMF) are used for denoising, also a novel filter Asymmetric Trimmed Median Filter(ATMF) is designed which outperforms compared to the filters designed earlier. Experimentation is carried by considering the dataset size of 700 noisy images. The average PSNR value of proposed system is...
A universal noise removal algorithm with an impulse detector
IEEE Transactions on Image Processing, 2005
We introduce a local image statistic for identifying noise pixels in images corrupted with impulse noise of random values. The statistical values quantify how different in intensity the particular pixels are from their most similar neighbors. We continue to demonstrate how this statistic may be incorporated into a filter designed to remove additive Gaussian noise. The result is a new filter capable of reducing both Gaussian and impulse noises from noisy images effectively, which performs remarkably well, both in terms of quantitative measures of signal restoration and qualitative judgements of image quality. Our approach is extended to automatically remove any mix of Gaussian and impulse noise.
Images are often corrupted by impulse noise in the procedures of image acquisition and transmission. Here,an efficient denoising scheme and its structure for the removal of random valued impulse noise in images.To achieve the goal at low cost,a low complexity architecture is proposed.I employ a PCA based technique to estimate the noisy pixels, and an edge preserving filter to reconstruct the intensity values of noisy pixels. Furthermore, an adaptive technology is used to enhance the effects of removal of impulse noise. PCA is used to estimate the noise and an edge preserving filter is used to enhance the image. Extensive experimental results demonstrate that the proposed technique can obtain better performance in terms of both quantitative evaluation and visual quality than the previous lower complexity methods.
International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016
This paper proposes a hybrid technique to remove impulse noise from digital images. In this approach the filtering operation is based on 33 neighborhood of pixel under consideration. During filtering, the properties of neighborhood are considered to check whether it is highly corrupted with noise, medium or only itself act as impulse. Based upon these properties a new hybrid technique has been proposed to process the pixel which further uses different schemes. The experiments have been performed at various noise levels on standard images as well as on real images. The results have been evaluated on the basis of metrics like Signal to noise ratio (SNR), Edge preservation index (EPI), Structure similarity index measure (SSIM), Multi scale structure similarity index measure (MS-SSIM) and Peak signal to noise ratio (PSNR). From the results, it has been observed that proposed technique has worked efficiently by preserving the edges and fine lines. To demonstrate the effectiveness of proposed technique, the results have also been compared with other well accepted denoising techniques
A Review of Decision Based Impulse Noise Removing Algorithms
Noises is an unwanted factor in digital image and videos, hiding the details and destroying image information. Hence denoising has great importance to restore the details and to improve the quality measures. This paper takes a look towards different type of noise found in digital images, Denoising domains, and classification of denoising filters. Some denoising filters like Median filter (MF), Adaptive median filter (AMF) and simple adaptive median filters (SAMF) are described and compared briefly. A new approach is proposed for video denoising using combination of median filters with multiple views.
Impulse Noise Removal in Digital Images
2017
The paper presents a solution for removing Salt and Pepper noise ie. Impulse noise from digital images. It does so using feedback alogrithm and adaptive window mechanism for find-ing the values at the corrupted pixels in the image. A modified median filter based on a previous noise detection stage is used. The paper also compares other state-of-the-art algorithms for the removal of salt and pepper noise and result comparison with our proposed algorithm. The results show that the filters proposed achieved good results, outperforming other state-of-the-art algorithms for low and medium noise ratios, and were comparable for very highly corrupted images.
Fast restoration of natural images corrupted by high-density impulse noise
EURASIP Journal on Image and Video Processing, 2013
In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures.
A Novel Technique to Improve Impulse Noise from Digital Images
2014
Digital Images are corrupted by different varieties of noises like Gaussian noise, Impulse noise etc. Impulses are salt and pepper noises which degrade the images quality with black and white spots. Here is an effective algorithm for effectively denoising the extremely corrupted image with the impulse noise. The proposed method works on the principle of duality. First adaptive median filter is applied and then contrast stretching with CLAHE is applied. The corrupted pixels are replaced by value of uncorrupted pixels in the filtering window. The proposed algorithm works well for images with high percentage of impulse noise. A comparison is made between proposed algorithm and other different median filtering algorithms.
Edge preserving restoration of random valued impulse noises (EPRRVIN)
2011 International Conference on Recent Trends in Information Systems, 2011
To remove random valued impulse noise from the digital images, a novel two step method has been proposed. The first step is to classify whether the center pixel in the 5 x 5 window is noisy or not, which is done using all neighbor directional weighted pixels in a 5 x 5 mask. The proposed algorithm performs simple arithmetic absolute differences on the pixels aligned in the four main directions with the center pixel. On detection of noisy pixels an advance median filter has been proposed for restoration of noisy pixel where a variable window consisting of 3 x 3 and 5 x 5 respectively have been used. To optimize the results, a 3-D search space has been used on user supplied parameters in a wide range. Proposed algorithm obtains better results in suppression of RVIN in digital images than the existing operators. The algorithm also preserves fine textures and details in the restored images. Keywords-All neighbor directional weighted pixels, decreasing rate of threshold in each iteration, iteration, image de noising, random valued impulse noise, threshold, variable window.