Noise-free Pixels Research Articles

Adaptively Directed Image Restoration Using Resilient Backpropagation Neural Network

In this modern era, visual data transmission, processing, and analysis play a vital role in daily life. Image denoising is the process of approximately estimating the original version of a degraded image. The presence of unexpected noise (e.g., fixed, random, and Gaussian) is the root cause of degradation, which has been reduced to some extent by many linear and non-linear filters based on a median value. The real issue is developing a strategy that should be generalized enough to effectively restore an image corrupted with multi-nature noise. Many researchers have developed novel concepts, but their tactics must acquire the highest performance in this area. This article proposes a constrained strategy for this problem, i.e., an adaptively directed denoising filter (ADD filter) based on a neural network. It consists of three major stages: training, filtering, and enhancing. First, we train a feed-forward back-propagation neural network on noisy and noise-free pixels for effective differentiation. Second, we apply a one-pass selective filter to the noisy image. The objective of this one-pass filter is to minimize noise using an adaptive median or directional filter based on density. Finally, the iterative directional filter is applied to the pre-processed image to enhance its visual quality. The extensive experiments depict that the proposed system has achieved better subjective results and improved local (structural similarity) and global (peak signal-to-noise ratio or mean square error) statistical measures.

Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising

In this paper, a convolved feature vector based adaptive fuzzy filter is proposed for impulse noise removal. The proposed filter follows traditional approach, i.e., detection of noisy pixels based on certain criteria followed by filtering process. In the first step, proposed noise detection mechanism initially selects a small layer of input image pixels, convolves it with a set of weighted kernels to form a convolved feature vector layer. This layer of features is then passed to fuzzy inference system, where fuzzy membership degrees and reduced set of fuzzy rules play an important part to classify the pixel as noise-free, edge or noisy. Noise-free pixels in the filtering phase remain unaffected causing maximum detail preservation whereas noisy pixels are restored using fuzzy filter. This process is carried out traditionally starting from top left corner of the noisy image to the bottom right corner with a stride rate of one for small input layer and a stride rate of two during convolution. Convolved feature vector is very helpful in finding the edge information and hidden patterns in the input image that are affected by noise. The performance of the proposed study is tested on large data set using standard performance measures and the proposed technique outperforms many existing state of the art techniques with excellent detail preservation and effective noise removal capabilities.

DTMF: Decision Based Trimmed Multimode Approach Filter for Denoising MRI Images

The brain MRI image denoising is a challenging and attracting field for young researchers because it enhances the quality of medical images. Salt and pepper noise is the most dangerous noise which reduces the accuracy of brain diagnosis, and it damages the brain medical images severely, that leads to neurologists to fix incorrect treatments or surgery. The pitfalls raised in the existing denoising methods are less Peak signal to noise ratio, high time consumption and incapable for enormous level of noise range. Hence, this research proposes a novel denoising filter which is entitled as ‘Decision based Trimmed Multimode approach oriented Filter (DTMF)’ for salt and pepper noise removal. Herein, the noise removal section is branched into six steps which efficiently reduce noises based on multimode of majority strength. The main concepts used in this research are viz. decision based approach, trimming process, majority of intensity, median, mean, dynamic windows and Square shaped Exemplar Modeled Patch Mechanism (SEMPM). The essential contributions of this approach are i) designing rule set for majority strength structured multimode denoising, ii) computation of majority property oriented parameters like majority-instance, majority strength and majority value, iii) novel SEMPM mechanism to predict noise-free data. The novel SEMPM mechanism grants a solution for the prediction of noise-free pixel in account of the noisy pixels whose surrounding window is completely packed by noisy data. The proposed decision-based approach removes the salt and pepper noise with high peak signal to noise ratio even for huge noise range, with reasonable time consumption.

Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter

Acquisition of images from electronic devices or Transmission of the image through any medium will cause an additional commotion. This study aims to investigate a framework for eliminating impulse noise from grayscale and medical images by utilizing linear regression and a mean filter. Linear regression is a supervised machine learning algorithm that computes the value of a dependent variable based on an independent variable. The value of the recuperating pixel is measured using a curve-fitting, direction-based linear regression approach or applying a mean filter to the noise-free pixels. The efficiency of the proposed technique experiments with benchmark test images and the images of the USC-SIPI and TESTIMAGES data sets. Peak signal-to-noise ratio and structural similarity index metrics are determined to prove the performance of the proposed method. The evaluated results, when compared with the seven recent state-of-the-art techniques, show the superiority of the proposed method in terms of visual quality and accuracy. The proposed model achieves an average PSNR value of 65.21dB and an SSIM value of 0.999 for the reconstruction of medical images, proving its accuracy and efficiency. The impulse noise restoration process helps the radiologist get a clear visual clarity of the DICOM image for diagnosis purposes.

An Efficient Procedure for Removing Salt and Pepper Noise in Images

In this paper, we propose an efficient algorithm for removing salt and pepper noise in images. The process of denoising is implemented in two stages: noise detection followed by noise removal. For noise detection, two extreme intensity values in an image are used to detect possible “noise pixels”. For noise removal, the switching mechanism only selects “noise pixels” for processing to avoid altering any fine image details, and only the identified noise-free pixels are used to achieve better denoising performance. Two filtering techniques, the edge-preserving filtering (EPF) and the extremum-compressing median filtering (ECMF), are employed for edge-preserving and noise removal. The EPF provides higher correlation between the corrupted pixel and neighborhood pixel, which gives rise to better edge preservation. The ECMF can yield an appropriate estimation by selecting the median pixel from the noise-free pixels of current filtering window. The proposed algorithm is tested on different images and provides a better restoration performance over some of the salt and pepper noise filters.

Random-Valued Impulse Noise Detection and Removal based on Local Statistics of Images

Random-valued impulse noise removal from images is a challenging task in the field of image processing and computer vision. In this paper, an effective three-step noise removal method was proposed using local statistics of grayscale images. Unlike most existing denoising algorithms that assume the noise density is known, our method estimated the noise density in the first step. Based on the estimated noise density, a noise detector was implemented to detect corrupted pixels in the second step. Finally, a modified weighted mean filter was utilized to restore the detected noisy pixels while leaving the noise-free pixels unchanged. The noise removal performance of our method was compared with 10 well-known denoising algorithms. Experimental results demonstrated that our proposed method outperformed other denoising algorithms in terms of noise detection and image restoration in the vast majority of the cases.

Generalized framework for the design of adaptive fractional-order masks for image denoising

This paper proposes a generalized fractional differential (GFD) mask which incorporates various fractional-order kernels such as power-law kernel, exponentional kernel, and Mittag-Leffler kernel, corresponding to Riemann-Liouville (RL)/Caputo, Caputo-Fabrizio (CF), and Atangana-Baleanu (AB) fractional-order differentials, respectively. Furthermore, a generalized fractional integral (GFI) mask incorporating RL and AB fractional integrals is put forward. The proposed generalized fractional masks provide a more flexible and appropriate tool for image enhancement and image denoising applications, and hence, avoids the construction of different masks for each kernel separately. Additionally, a generalized fractional integral and fractional differential based adaptive algorithm for image denoising (GFIFD-AA) is proposed. The proposedGFIFD-AA incorporates a novel noise detection method (NDM) for the detection of salt and pepper (SP) noise in images, which exhibits a profound advantage in avoiding misclassification of original pixels as noisy pixels when original image itself has some pixels with intensity values 0 or 255. The detected noisy pixels are processed by utilizing the proposed GFI-based adaptive mask (GFI-AM). The noise-free pixels are further updated by utilizing the proposed GFD-based adaptive mask (GFD-AM) so as to enhance the details of the image. Several standard and medical images of different characteristics are examined to evaluate the performance of the proposed approach on images affected by SP noise at various noise densities (i.e., 10%-95%). Furthermore, the performance of the proposed GFI mask is also tested for images affected by Gaussian noise and is compared against conventional fractional-order masks. The simulation results based upon several quantitative parameters validate the effectiveness of the proposed method against conventional methods.

Fuzzy Based Fast Non Local Mean Filter to Denoise Rician Noise

Abstract MRI images suffer from a type of multiplicative noise called Rician noise. The traditional smoothing filters are not effective to reduce this type of noise. Non local Means (NLM) filters with bias correction is a promising technique for such type of signal dependent noise. The NLM denoising is based on self-similarity and is computationally very expensive. Many researchers are working to improve its time complexity. In most cases there is a tradeoff between speed and quality of denoised image. This paper proposes a speed enhanced NLM method that use integral image and fuzzy Jacord similarity measure to find the self-similarity of sub windows. This helps to find the weights of similar pixel at a faster rate than the traditional NLM algorithm and more accurately than the existing fast NLM method. Consequently, these similar pixels are used to generate the noise free pixels. At the end the conventional bias subtraction method is used as post processing step. The proposed scheme is tested with the standard brain web data set with different noise levels and compared with existing Fast NLM techniques and basic NLM using Root mean square error (RMSE), peak signal noise ratio (PSNR), Structure similar index (SSIM), quality index and computational time parameters methods. The proposed method gives better result than existing Fast NLM technique with high density Rician noise in the image and is 20 times faster than traditional NLM.

Improved Fuzzy Based Non-Local Mean Filter to Denoise Rician Noise

Nowadays MRI has become an important tool to diagnose medical conditions but there is a growing need for a denoise image produced. Rician noise is one of the major challenges in MRI. So the nonlocal means (NLM) filter has gained popularity to denoise medical images as it gives excellent results. In the present work, an Improved Fuzzy-based Non-Local Mean Filter is proposed for denoise Rician noise. In the proposed method the first step is to find the non-local similar pixel in the image using fuzzy function. Then these similar pixels are used to generate noise-free pixels. The above approach is tested with real data and the results are compared with existing Fuzzy techniques by using root mean square error, structural similarity index measure, and peak signal-noise ratio (PSNR) methods. This technique gives better result than the existing Fuzzy Non-Local Mean technique with both high and low-density Rician noise in the image

Significance driven inverse distance weighted filter to restore impulsive noise corrupted X-ray image

This paper presents a novel significance driven inverse distance weighted (SDIDW) filter for the impulsive noise removal in the X-ray images. The proposed SDIDW filter restores the noisy pixel using minimum number of nearest noise-free pixels to achieve good estimation while exhibiting low computational complexity. In the proposed filter, higher priority (weight) is given to nearest pixels compared to distant pixels and only sufficient nearest noise free pixels are determined to estimate the value of noisy pixel. A high level analysis of the computation complexity at varying noise density is done which shows that proposed SDIDW filter provides significant reduction in computation complexity over the adaptive median filters. Finally, the performance of the proposed filter is evaluated and compared over the state-of-the-art impulse noise removal techniques for varying noise density (wide range 10–90% and very high noise density range 91–99%). The experimental results on medical images demonstrate significant improvement in filtered images quality by the proposed filter over the state-of-the-art filters at each sample of noise density with small computational complexity.

Impulse noise removal in color image sequences using fuzzy logic

This paper presents a two-step fuzzy filter to remove impulse noise from a color image sequence in RGB color space. The primary filter recognizes the pixels corrupted by impulse noise. It also computes the extent of noise and afterward rectifies them. The output of the first filter acts as an input to the secondary filter and further refines the outcome to give the final output. Excellent alignment is seen between noise removal and structure conservation of an image due to the classifying nature of the algorithm. To minimize blurring, noisy pixels are exclusively rectified and noise-free pixels remain intact. The proposed filter is a 3-D Spatio-temporal filter that considers spatial, temporal as well as color information. A pixel of one color component is compared to its neighboring pixels within the same frame and with the corresponding pixels in neighboring frames. It is likewise compared with the pixels of the other two color components. Peak Signal to Noise Ratio (PSNR) and structural similarity index (SSIM), Miss Detection (MD), and False Alarm (FA) rates are utilized as a performance metric. The experimental result of several color image sequences demonstrates the efficacy of the proposed fuzzy filter both qualitatively and quantitatively.

Fuzzy based fast non local mean filter to denoise Rician noise

MRI images suffer from a type of multiplicative noise called Rician noise. The traditional smoothing filters are not effective to reduce this type of noise. Non local Means (NLM) filters with bias correction is a promising technique for such type of signal dependent noise. The NLM denoising is based on self-similarity and is computationally very expensive. Many researchers are working to improve its time complexity. In most cases there is a tradeoff between speed and quality of denoised image. This paper proposes a speed enhanced NLM method that use integral image and fuzzy Jacord similarity measure to find the self-similarity of sub windows. This helps to find the weights of similar pixel at a faster rate than the traditional NLM algorithm and more accurately than the existing fast NLM method. Consequently, these similar pixels are used to generate the noise free pixels. At the end the conventional bias subtraction method is used as post processing step. The proposed scheme is tested with the standard brain web data set with different noise levels and compared with existing Fast NLM techniques and basic NLM using Root mean square error (RMSE), peak signal noise ratio (PSNR), Structure similar index (SSIM), quality index and computational time parameters methods. The proposed method gives better result than existing Fast NLM technique with high density Rician noise in the image and is 20 times faster than traditional NLM.

Improved probabilistic decision‐based trimmed median filter for detection and removal of high‐density impulsive noise

This study focuses on the detection and expulsion of noisy pixels from an image contaminated by impulsive noise. A noise detection approach is developed to avoid the misinterpretation of noise-free pixel as noisy. In order to design the noise removal algorithm, a probabilistic decision-based improved trimmed median filter (PDITMF) algorithm is proposed which is intended to work out the conflict related to the even number of noise-free pixels in the trimmed median filter. It deploys two new estimation techniques for de-noising, namely, improved trimmed median filter (ITMF) and patch else ITMF (PEITMF) as per noise density. At last, the noise detection approach is applied in the proposed PDITMF to build up a new technique called a probabilistic decision-based adaptive improved trimmed median filter (PDAITMF) algorithm. The proposed algorithms, PDITMF and PDAITMF experiment with many standard sample images. Simulation results show the proposed algorithms are capable of detecting and de-noising the contaminated image very efficiently and have a better visual representation. Under the authors' knowledge, the PDAITMF outperforms recently reported algorithms in context to peak signal-to-noise ratio as well as an image enhancement factor with the lower execution time at all noise densities.

Four stage median-average filter for healing high density salt and pepper noise corrupted images

This paper introduces a novel four stage filter algorithm to ameliorate images corrupted by very high density salt-and-pepper noise. The proposed algorithm exhibits two parallel trimmed median filters (TMF) at the initial stage followed by a masking logic that selects denoised pixel based on the priority. To reduce the blurring effect, higher priority is given to TMF with small window size. In the absence of noise-free pixels, the current extreme pixel is left unchanged at the first stage. Further, the denoising of unprocessed extreme pixels is done with TMF of large size window at the second stage. The remaining noisy pixels are improved by the running average filter at the third stage. Finally, the last stage handles the noisy pixels at the boundary and rare scenario. Since the proposed filter utilized non-extreme pixels to estimate denoinsed pixels value, it effectively eliminates salt and pepper noise along with better edge preservation. The performance analysis of the proposed filter is carried out with various benchmark images for varying noise density. The experimental results demonstrate on an average improvement of 2.09 dB (0.018) and 1.06 dB (0.0478) of PSNR (SSIM) respectively for wide (10% - 90%) and very-high (90% - 98%) noise density ranges over state-of-the-art filters.

Image enhancement using deep-learning fully connected neural network mean filter

Improving the quality of a noisy image is important for image applications. Many novel schemes pay great efforts in the removal of impulse noise. Most of them restore noisy pixels only by using the neighboring noise-free pixels, but the relationship between a noisy image and its noise-free one, which denotes the clean image not corrupted by noise, is ignored. So the reconstruction quality cannot be further improved. In this study, we employ a deep-learning fully connected neural network (FCNN) to select top N candidates of neighboring un-corrupted pixels for the restoration of a center noisy pixel in an analysis window. Hence, the mean value of the gray levels of these top N pixels is computed and employed to replace the noisy pixel, yielding the noisy pixel being restored. The experimental results reveal that the proposed deep-learning FCNN mean filter can remove impulse noise effectively in corrupted images with different noise densities.

Probabilistic Decision Based Improved Trimmed Median Filter to Remove High-Density Salt and Pepper Noise

This paper focuses on the removal of salt and pepper noise from a contaminated image. A Probabilistic Decision Based Improved Trimmed Median Filter (PDITMF) is proposed here. The proposed PDITMF algorithm resolves the conflict regarding an even number of noise free pixel of Trimmed Median Filter. The proposed algorithm makes use of two estimation techniques for de-noising, namely, Improved Trimmed Median Filter (ITMF), and Patch Else Improved Trimmed Median Filter (PEITMF) depending upon noise density. The algorithm experiments with many standard sample images. Simulation results show the proposed algorithm is capable of de-noising the image very efficiently. The algorithm has a better visual representation and it outperforms the existing well-known algorithms in context to peak signal-to-noise ratio (PSNR) as well as image enhancement factor (IEF) with lower execution time (ET) at all noise densities.

Iterative deviation filter for fixed-valued impulse noise removal

An iterative deviation filter for fixed-valued impulse noise removal is proposed, with the aim to overcome the defects of existing filters, and further improve the denoising performance. In the proposed filter, a noise detection method based on the extreme intensity values and the deviation of neighbor pixels is proposed, i.e., the pixels with the extreme intensity and differ greatly from the mean of neighbor pixels, are identified as noises. A noise removal method based on the minimum deviation of neighbor pixels is proposed, i.e., the intensity of one neighbor noise free pixel, which is closest to the mean of neighbor noise free pixels, is used as estimated intensity of noisy pixel under consideration. Furthermore, the noise removal strategy performs iteratively and takes full advantage of the previous denoising results. Simulation results show that the proposed method has better denoising performance than the existing distinguished filters in terms of visual representation, peak signal to noise ratio and structural similarity index.

Impulse Noise Detection and Removal Method Based on Modified Weighted Median

Impulse noise generally occurs because of bit errors in progression of image acquisition and transmission. It is well known that median filtering method is an impulse noise removal method. Lots of modified median filters have been proposed in the last decades to improve the methods for noise suppression and detail preservation, which have their own deficiencies while identifying and restoring noise pixels. In this article, after deeply analyzing the reasons, such as decreased noise detection and noise removal accuracy that forms the basis of the deficiencies, this article proposes a modified weighted median filter method for color images corrupted by salt-and-pepper noise. In this method, a pixel is classified into either “noise free pixel” or “noise pixel” by checking the center pixel in the current filtering window with the extreme values (0 or 255) for an 8-bit image using noise detection step. Directional differences and the number of “good” pixels in the current filtering window modify the detected noise pixels. Simulation effects on considered test images reveal the proposed method to be improved over state-of-the-art de-noising methods in terms of PSNR and SSIM with pictorial comparative analysis.

High Density Video Impulse Noise Reduction Scheme Based on Spatially Growing Modified Median Filter

In this paper a spatially growing modified median filter method has been proposed for the restoration of digital videos which are adulterated by the saturated impulse noise i.e., salt-and pepper noise. The proposed denoising process executes filtering action only to the corrupted pixels in the video keeping noise free pixels in the video unharmed. The current study has used a spatially growing window method for the exclusion of high density noise present in the digital videos. It has used sliding window of increasing dimension centered at any pixel and sequentially swapped the noise corrupted pixels by the median value of the trimmed window. However, if the whole pixels in the window are noise corrupted then the dimension of sliding window is enlarged in order to obtained the noise free pixels for median computing. At the same time, this process has been found to be capable to removing the high density salt and pepper noise and also well preserved the fine details of the videos. Experimentally, it has been found that the proposed method generate enhanced PSNR and SSIM results.

Generalized fractional derivative based adaptive algorithm for image denoising

This paper presents a new image denoising algorithm based on fractional filters. The fractional filters are derived using a newly introduced fractional operator. The proposed algorithm identifies the noisy pixels based on pixel-density and upgrades them by an adaptive fractional integral mask. To maintain the correlation and recover the lost information, the noise-free pixels are also processed by an adaptive fractional differential mask. We formulate the order function for the fractional mask with the help of gradient features and variance of the image. The algorithm is applied to standard images of different characteristics. The experimental results are compared with some other existing techniques. Evaluation parameters and visual perceptions show that the proposed method performs better than most of the discussed methods. The proposed approach is applicable for image denoising due to its applicability over different types of noises and denoising performance.