Current Processing Window Research Articles

VLSI Architecture of Decision Based Adaptive Denoising Filter for Removing Salt & Pepper Noise

A new Decision Based Adaptive Denoising Filter (DBADF) algorithm & hardware architecture are proposed for restoring the digital image that is highly corrupted with impulse noise. The proposed DBADF detects only the corrupted pixels and that pixel is restored by the noise-free median value or previous value based upon the noise density in the image. The proposed DBADF uses a window initially and adaptively goes up to window based on the noise corruption more than 50% by impulse noise in the current processing window. The proposed architecture was found to exhibit better visual qualitative and quantitative evaluation based on PSNR, IEF, EKI, SSIM, FOM, and error rate. The DBAMF architecture also preserves the original information of digital image with a high density of salt & pepper noise, when compared to many standard conventional algorithms. The proposed architecture has been simulated using the VIRTEX7 FPGA device and the reported maximum post place and route frequency are 149.995MHz and the dynamic power consumption is 179mW.

A Decision Based Neighbourhood Referred Asymmetrically Trimmed Modified Trimean for the Removal of High Density Salt and Pepper Noise in Images and Videos

A Decision Based Neighbourhood Referred Asymmetrically Trimmed Modified Trimean for the Removal of High Density salt and pepper noise in Images and videos is proposed. The proposed algorithm initially checks for the outliers in a 3 × 3 neighbourhood. If the processed pixel is noisy then check for the presence of noisy pixels with the 4 neighbours; If the 4 neighbours are found to hold outliers then mean of the 4 neighbours will replace the output. If the 4 neighbours are not noisy then the output is replaced by asymmetrically trimmed Modified Trimean. If all the pixels of the current processing window are noisy then the mean of all elements will replace the processed pixel. If the processed pixel does not hold the outlier then the pixel is termed as not noisy and left unaltered. The proposed algorithm exhibit excellent noise elimination capability with enhanced edge preservation capability. The algorithm was tested on a standard database and the results of the proposed algorithm were compared to 16standard and existing algorithms. The proposed algorithm exhibit excellent results in terms of both Quantitative and qualitative measures.

Ultrasound Speckle Reduction Based on Histogram Curve Matching and Region Growing

The quality of ultrasound scanning images is usually damaged by speckle noise. This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm. Unlike single statistical moment-based speckle reduction algorithms, this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability. The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance. Then, according to the similarity value and tissue characteristics, the entire image is divided into several levels of speckle-content regions, and adaptive smoothing is performed based on these classification characteristics and the corresponding window size determined by the proposed region growing technique. Tests conducted from phantoms and in vivo images have shown very promising results after a quantitative and qualitative comparison with existing work.

High-density salt-and-pepper noise removal using adaptive weighted kriging interpolation filter

An adaptive weighted kriging interpolation filter for removal of high-density salt-and-pepper noise (SPN) in images is proposed. The proposed scheme introduces a method for computing the estimation value of a noisy pixel in the center of the processing window. Different from the existing adaptive decision based on kriging interpolation algorithm, the proposed kriging interpolation for different adaptive windows cares about the action of not only the Euclidean distance between nonnoisy pixels but also the size of the current processing window. Therefore, the corrupted pixel is replaced by the inverse filtering-radius weighted sum of kriging interpolations for different adaptive windows. The final processing window is required to include at least three noncorrupted pixels. The proposed algorithm is extensively evaluated on a variety of benchmark images and the experimental results show that it outperforms several standard and popular algorithms in terms of visual quality and quantitative results.

An Adaptive Non Linear filter based on Median of Minimum Distance for Salt and Pepper Noise Removal in Mammogram Images

Background: Mastography is a strong screening and diagnostic tool for the primitive detection of cancer in the breasts. The acquired mammogram images or stored mammogram images are often corrupted by the outliers. These outliers must be eliminated before microcalcification. Method: In the proposed scenario, an adaptive filter that uses median of minimum distance from its neighbourhood is used to eliminate the salt and pepper noise. The minimum Euclidean distance between each of the non noisy pixels allows the algorithm to choose a better value to replace the corrupted pixels. The algorithm terms the pixel to be corrupted if it holds 0 or 255; otherwise, the pixel is termed uncorrupted. The algorithm uses 3x3 window initially and increases the window size, if the entire window consists of outliers. If the uncorrupted number of pixels inside the processing window is at least 2, then the corrupted pixel is replaced by the median of minimum Euclidean distance between each non noisy pixel and the corrupted pixel. If the number of non noisy pixels inside the current processing window is 1, then the corrupted pixel is replaced by it. Results: The Mini Mias database is used to assess the performance of the (ADBMEDUTMF) Adaptive Decision based minimum Euclidean distance unsymmetrical Trimmed median filter with the other standard and recently proposed algorithms. The ADBMEDUTMF proved efficient in removing the corrupted pixels and found to operate well even at noise densities as high as 90% without inducing any artifacts. Conclusion: Exhaustive test results suggest that the ADBMEDUTMF algorithm exhibits very good noise suppression characteristics with minimum error and higher information preservation capability even at very high noise densities in different mammogram images. This makes the algorithm suitable for low level processing. Keywords: Salt and pepper noise, euclidean distance, median of minimum distance filter, minimum error, pixel.

An adaptive decision based interpolation scheme for the removal of high density salt and pepper noise in images

An adaptive decision based inverse distance weighted interpolation (DBIDWI) algorithm for the elimination of high-density salt and pepper noise in images is proposed. The pixel is initially checked for salt and pepper noise. If classified as noisy pixel, replace it with an inverse distance weighted interpolation value. This interpolation estimates the values of corrupted pixels using the distance and values nearby non-noisy pixels in vicinity. Inverse distance weighted interpolation uses the contribution of non-noisy pixel to the interpolated value. The window size is varied adaptively depending upon the non-noisy content of the current processing window. The algorithm is tested on various images and found to exhibit good results both in terms of quantitative (PSNR, MSE, SSIM, Pratt’s FOM) and qualitative (visually) at high noise densities. The algorithm performs very well in restoring an image corrupted by high-density salt and pepper noise by preserving fine details of an image.

An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images

An adaptive decision based kriging interpolation technique for the removal of high density salt and pepper noise in images is proposed. The algorithm isolates the non noisy pixels and process only noisy pixel. The corrupted pixel is replaced by a value that is interpolated using the weights calculated using semi-variance between the corrupted pixels and the non noisy pixels in a confined neighborhood. The proposed technique requires at least three non noisy pixels in a current processing window, failing which the window increases adaptively. The proposed technique was found to exhibit good of quantitative evaluation and visual quality. The proposed algorithm also preserves image information with accurate noise detection when compared to many standard and existing algorithms.

Efficient decision based algorithm for the removal of high density salt and pepper noise in images

In this paper, an efficient decision based scheme is proposed for the restoration of grayscale and colour images that are heavily corrupted by salt and pepper noise. The processed pixel is examined for 0 or 255; if found true, then it is considered as noisy pixel else not noisy. If found noisy the four neighbours of the noisy pixels are checked for 0 or 255. If all the four neighbours of the corrupted pixel are noisy, the mean of the four neighbours replaces the corrupted pixel. If any of the four neighbours is a non-noisy pixel, calculate the number of corrupted pixels in the current processing window. If the count is less than three then the noisy pixel is replaced by an unsymmetrical trimmed median. If the current window has more than three noisy pixels, then unsymmetrical trimmed mean replaces the corrupted pixels. If all the pixels of the current processing window are noisy then instead of unsymmetrical trimmed mean, global mean of the image is replaced as output. The uncorrupted pixel is left unchanged. The proposed algorithm is tested on various grayscale and colour images and found that it gives excellent PSNR, high IEF and lowest MSE. Also it consumes average time with excellent edge preservation even at higher noise densities. The quality of the results of proposed algorithm is superior when compared to the various state of the art methods.

Hybrid Approach of Efficient Decision-Based Algorithm and Fuzzy Logic for the Removal of High Density Salt and Pepper Noise in Images

In this paper, this hybrid approach of efficient decision-based scheme and fuzzy logic are proposed for the restoration of gray scale and color images that are heavily corrupted by salt and pepper noise. The processed pixel is examined for 0 or 255; if found true, then it is considered as noisy pixel else not noisy. If found noisy the four neighbors of the noisy pixels are checked for 0 or 255. If all the four neighbors of the corrupted pixel are noisy, the mean of the four neighbors replaces the corrupted pixel. If any of the four neighbors is a non-noisy pixel, the number of corrupted pixels is calculated in the current processing window. If the count is less than three, then the noisy pixel is replaced by an unsymmetrical trimmed median. If the current window has more than three noisy pixels, then unsymmetrical trimmed mean replaces the corrupted pixels. If all the pixels of the current processing window are noisy then instead of enhanced decision-based algorithm, the fuzzy membership function of the window is replaced as output processing pixel. The uncorrupted pixel is left unchanged. The proposed algorithm is tested on various gray scale and color images and found that it gives excellent PSNR, high IEF and lowest MSE. Also it preserves the image features like the edges and color components at higher noise densities. The quality of the results of proposed algorithm is superior when compared to the various existing state-of-the-art methods.

Decision-based neighborhood-referred unsymmetrical trimmed variants filter for the removal of high-density salt-and-pepper noise in images and videos

A fixed $$3\times 3$$ decision-based algorithm is proposed for the enhancement of images, and videos that are heavily corrupted by salt-and-pepper noise are proposed. The algorithm uses unsymmetrical trimmed variants for the noise removal. The corrupted pixel is replaced based on the number of non-noisy pixel in the current processing window. The proposed algorithm was applied on various grayscale, and videos that gave excellent peak signal-to-noise ratio, high image enhancement factor, low mean square error, and very good SSIM with excellent edge preservation even at high noise densities. If all the pixels of the current processing window are noisy, then instead of unsymmetrical midpoint, global trimmed mean of the image is replaced as output. The proposed algorithm shows excellent noise suppression capability, when compared to standard and existing filters in terms of both qualitative and quantitative measures at highly noisy environment.

Performance Analysis of Unsymmetrical Trimmed Median as Detector on Image Noises & its FPGA Implementation

This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.