Noisy Pixels Research Articles

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.

Fingerprint Salt & Pepper Noisy Image Enhancement with Threshold Method

We present an adaptive threshold method for salt and pepper noisy fingerprint images. At first, we applied a threshold algorithm to detect noisy and corrupt pixels. Fingerprint image optimized with window filter produced pixels values. We measured the quality of the fingerprint images with peak signal to noise ratio and other efficient noise removing techniques were compared with it. The result shows improving performance of our method with remove salt and pepper noise from a fingerprint image. Our present work is useful to reduce or minimize medium level noise in fingerprint image. In future, we will work to reduce more than 80 percent noise level in fingerprint image.

High Density Impulse Noise Removal from Color Images by K-means Clustering based Detection and Least Manhattan Distance-oriented Removal Approach

Removal of impulse noise from color images is a stringent job in the arena of image processing. Impulse noise is fundamental of two types: Salt and pepper noise (SAPN) and Random valued impulse noise (RVIN). The key challenge in impulse noise removal from color images lies in tackling out the randomness in the noise pattern and in handling multiple color channels efficiently. Over the years, several filters have been designed to remove impulse noise from color images, but still, the researchers face a stringent challenge in designing a filter effective at high noise densities. In this study, a combination of K-means clustering-based detection followed by a minimum distance-based approach for removal is taken for high-density impulse noise removal from color images. In the detection phase, K-means clustering is applied on combined data consisting of elements from designated 5 × 5 windows of all the planes from RGB color images to segregate noisy and non-noisy elements. In the removal phase, noisy pixels are replaced by taking the average of medians of all non-noisy pixels and non-noisy pixels under 7 × 7 windows residing at least Manhattan distance from the inspected noisy pixel. Performance of the proposed method is evaluated and compared up against the latest filters, on the basis of well-known metrices, such as Peak signal to noise ratio (PSNR) and Structural similarity index measurement (SSIM). Based on these comparisons, the proposed filter is found superior than the compared filters in removing impulse noise at high noise densities.

Image Impulse Noise Removal Using Cascaded Filtering Based on Overlapped Adaptive Gaussian Smoothing and Convolutional Refinement Networks

Impulse noise is often introduced to images when captured through image sensors due to sharp and sudden disturbances in the image signal, analog-to-digital converter errors, sensor temperature, etc., severely degrading their visual quality. Therefore, it is essential to develop an effective method to remove image noise. We propose a novel image denoising method for “salt-and-pepper” (SP) noise, using cascaded filtering based on overlapped adaptive Gaussian smoothing (OAGS) and the convolutional refinement networks (CRNs). First, the noisy input image can be preliminarily denoised by OAGS, where the noisy pixels are removed and recovered. The CRNs refine the result by restoring fine details for the denoised image. Through extensive experimental results, we demonstrate the proposed method substantially outperforms other state-of-the-art denoising methods, especially for high-density SP noise.

Novel Adaptive Binarization Method for Degraded Document Images

Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast, bleed-through, and nonuniform illumination effects. Unlike the existing baseline thresholding techniques that use fixed thresholds and windows, the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better binarization. To enhance a low-contrast image, we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and, simultaneously, increasing pixel contrast at edges or near edges, which results in an enhanced image. For the enhanced image, we propose a new method for deriving adaptive local thresholds for dynamic windows. The dynamic window is derived by exploiting the advantage of Otsu thresholding. To assess the performance of the proposed method, we have used standard databases, namely, document image binarization contest (DIBCO), for experimentation. The comparative study on well-known existing methods indicates that the proposed method outperforms the existing methods in terms of quality and recognition rate.

Gaussian bilateral filtered discrete Hartley feature transformation based infomax boosting for hyperspectral image classification

Hyperspectral Image (HSI) is a spatially sampled image collected from a lot of neighboring narrowed spectrum bands via hyperspectral sensor. The process of HSIs is a difficult obligation because of the high-dimensional image features. Conventional techniques mainly utilize hand-made characteristics that are not fit for classification. For the purpose of enhancing the correctness and reducing the overall time complexity, an efficient technique called Gaussian Bilateral Filtered Hartley Feature Transformation Based Infomax Boost Classification (GBFHFT-IBC) is introduced. The input hyperspectral image is divided into the number of spectral bands. The proposed GBFHFT-IBC method includes three fundamental procedures, which include preprocessing, characteristics classification and extraction. Preprocessing is carried out using a Gaussian kernelized bilateral filter that replaces the pixel intensity from the picture with the weighted to take the position of the intensity of every pixel from the picture with the prejudiced average of neighboring pixels resulting in it removes the noisy pixels and improve the image quality. After preprocessing, the different characteristics like texture, color and shape are extracted from the image using the Discrete Hartley transformation technique. With the extracted features, the Infomax Boost method is applicable when classifying different scenes from the spectrum band, which has to be grouped into various categories with the top categorization accuracy that consider the minimal error rates. The experiments are done using the hyperspectral picture dataset composed of various factors like categorization accuracy, PSNR, positive and negative rate, and timeline complexity considering the number of spectral bands. The observed result shows that the presented GBFHFT-IBC method improves the hyperspectral image classification correctness, PSNR and minimized negative and positive rates, which include timeline complexity compared to using the sophisticated techniques.

Impulse denoising based on noise accumulation and harmonic analysis techniques

In the article, we propose a robust impulse denoising method based on noise accumulation and harmonic analysis techniques (NAHAT filter). Noise accumulation technique is used to improve the ability to detect noisy pixels, and a harmonic function is used to gain the accuracy of restoring gray values of the detected noisy pixels. The proposed method can remove impulse noise very effectively even under high-density impulse noise levels. Moreover, the proposed method can preserve image structures such as the edges and textures well. Based on experimental results, the proposed method obtains good performance under various noise levels as well as outperforms other similar state-of-the-art impulse denoising methods.

An Iterative Weighted-Mean Filter for Removal of High-Density Salt-and-Pepper Noise

Salt-and-pepper noise, which is often introduced by sharp and sudden disturbances in the image signal, greatly reduces the quality of images. Great progress has been made for the salt-and-pepper noise removal; however, the problem of image blur and distortion still exists, and the efficiency of denoising requires improvement. This paper proposes an iterative weighted-mean filter (IWMF) algorithm in detecting and removing high-density salt-and-pepper noise. Three steps are required to implement this algorithm: First, the noise value and distribution characteristics were used to identify the noise pixels, effectively improving the accuracy of noise detection. Second, a weighted-mean filter was applied to the noisy pixels. We adopted an un-fixed shape symmetrical window with better detail preservation ability. Third, this method was performed iteratively, avoiding the streak effect and artifacts in high noise density. The experimental results showed that IWMF outperformed other state-of-the-art filters at various noise densities, both in subjective visualization and objective digital measures. The extremely fast execution speed of this method is quite suitable for real-time processing.

Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images

It is essential to restore digital images corrupted by noise to make them more useful. Many approaches have been proposed to restore images affected by fixed value impulse noise, but they still do not perform well at high noise density. This paper presents a new method to improve the detection and removal of fixed value impulse noise from digital images. The proposed method consists of two stages. The first stage is the noise detection stage, where the difference values between the pixels and their surrounding pixels are computed to decide whether they are noisy pixels or not. The second stage is the image denoising stage. In this stage, the original intensity value of the noisy pixels is estimated using only their first-order and second-order neighborhood pixels. These neighboring orders are based on the Euclidean distance between the noisy pixel and its neighboring pixels. The proposed method was evaluated by comparing it with some of the recent methods using 50 images at 18 noise densities. The experimental results confirm that the proposed method outperforms the existing filters, excelling in noise removal capability with structure and edge information preservation.

Image enhancement using convolutional neural network to identify similar patterns

An image may be disturbed by impulse noise during transmission or acquisition. To effectively restore the disturbed image is important for the applications of image processing. This study aims at enhancing the disturbed images by using the convolutional neural network (CNN) to identify similar patterns for the restoration of noisy pixels. In the training phase, each noisy pixel is analysed and compared with the noise-free image to find the closest neighbouring pixels. The pixels in a local window form a micro-pattern. All the captured micro-patterns, whose centre pixel is noisy, become a dataset for the training of a position CNN. The closest neighbouring pixel of a noisy image to the centre one of the noise-free image at the same position of each micro-pattern is selected to be the target. In the enhancement phase, a noisy micro-pattern, where the centre pixel is noisy, is input into the trained position CNN. The top N pixels are recognised and averaged to replace the grey level of the centre pixel. An enhanced pixel is obtained. The experimental results show that the position CNN can well recognise the similar neighbouring pixels and effectively enhance the noisy pixels in an image disturbed by salt-and-pepper noise.

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.

Bacterial Foraging-Fuzzy synergism based Image Dehazing

The proposed work introduces a novel Bacterial Foraging (BF)-Fuzzy synergism based dehazing method to enhance the visibility of degraded hazy images. The proposed method performs contrast enhancement, edge & noise detection, noise removal and edge-sharpening (all the four important aspects of dehazing). In the first step of the method, each pixel of the degraded V channel of hazy image is enhanced using unique defuzzified mapping constant which is evaluated depending upon its Haze-concentration and Log-sigmoid transformation function values to prevent poor or over-enhancement. Subsequently, in the next step simultaneous edge & noise detection is performed using BF algorithm in combination with one set of novel FI rules. These rules beside performing simultaneous edge & noise detection also evaluate edge-strengths of all possible edge directions. BF algorithm is used here to determine the most suitable direction for the movement of bacteria within each image patch. It also facilities the selection of true edge pixels by imposing constraints on defuzzified edge-strengths which eliminates any chances of false edge detection. Noisy pixels are filtered here according to their extent of corruption w.r.t each pixel located within their neighbourhood using another novel set of FI rules to prevent undesired edge-blurring. Finally, selected edges are sharpened using unique sharpening factors which are evaluated according to their respective edge-strength using a set of novel FI rules to prevent the occurrence of undesirable halo-artifacts in outputs. Comparative qualitative, quantitative and run-time complexity analyses’ results also proved the excellence of the proposed work over several state-of-the art methods.

Progressive path tracing with bilateral-filtering-based denoising

Path tracing can generate realistic images based on virtual 3D scene models, but the images are prone to be noisy. To solve this problem, we developed a novel denoising algorithm framework. Firstly, according to the relative mean square error of the noisy pixels, we introduced a progressive adaptive sampling strategy to optimize the distribution of samples. Next, to enhance the quality of the final reconstructed images, we designed an improved bilateral filtering algorithm with use of the gradient feature to obtain the noise-free images. Experimental results demonstrate that our framework outperforms the state-of-the-art path tracing denoising methods in terms of the visual quality, numerical error , and time cost.

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.

A novel decision-based adaptive feedback median filter for high density impulse noise suppression

The qualitative performances of the digital image processing methods are degraded due to the presence of impulse noise. The conventional median filter and its advanced versions somehow manage to remove the noise from image but cannot preserve the image details. In this paper, a novel decision based adaptive feedback median filter is proposed to suppress the high density noise and preserve the details of the image. The proposed method detects the corrupted or noisy pixels by analyzing the neighbours in a decisive manner, which is a challenging task for the different types of images and noise. It predicts a local threshold by analyzing the neighbours to decide the adaptive nature of the feedback median filter. The feedback mechanism is adapted to enhance the qualitative results. Various types of images and noise densities have been used to evaluate the performance of the proposed method. The qualitative and quantitative performances have been measured in terms of Peak Signal-to-Noise Ratio, Image Enhancement Factor and Structural Similarity Index. The experimental results show that the qualitative and quantitative performances are superior over existing methods and the computational time is comparable as well.

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.

An experimental study of relative total variation and probabilistic collaborative representation for iris recognition

Iris images collected under different conditions often suffer from specular reflections, cast shadows, motion blur, defocus blur, occlusion caused by eyelashes and eyelids, eyeglasses, hair and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, an iris recognition method based on relative total variation (RTV) and probabilistic collaborative representation is proposed. RTV uses the l1 norm regularization method to robustly suppress noisy pixels to achieve accurate iris localization, while probability collaborative representation maximizes the probability that the test sample belongs to each of the multiple classes. The final recognition rate is calculated based on the class having maximum probability. Experimental results using CASIA-V4-Lamp and IIT-Delhi V1iris image databases showed that the proposed method achieved competitive performance in both recognition accuracy and computational efficiency.

Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

Abstract In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.

Bi-Directional Filter for the Removal of Lines and Cracks in Images

In this paper, a method for the removal of noisy lines and cracks corrupted by different noise types is explored, using a cascade of filtering cycles based on the principle of symmetry among neighboring pixels. Each filtering cycle includes a filter in two perpendicular directions, one horizontal and the other vertical. Any pixel, to be deemed original, should have a number of symmetric pixels within its neighboring pixels greater than the number specified by the condition set for each direction in all the filters. Since the conditions of each filter increase gradually from one cycle to the next, it becomes more difficult for a noisy pixel to satisfy the filter conditions in each filtering cycle, while an original pixel can easily satisfy the conditions in all the filtering cycles. The reason is that a noisy pixel has a random value and therefore faces difficulty in finding a sufficient number of symmetric pixels in each direction, while an original one has a value correlated with the values of its neighboring pixels. Extensive simulation experiments prove that the proposed method efficiently detects and restores different noisy lines and cracks of different shape and thickness. Also, it retains the image details and outperforms other well-known algorithms, both objectively and subjectively. More specifically, the proposed algorithm achieves restoration performance better than the other known methods by ≥0.81dB in all simulation experiments.