Impulse Noise Removal Algorithm Research Articles

A novel statistical golden ratio based adaptive high density impulse noise removal algorithm

A novel statistical golden ratio based adaptive high density impulse noise removal algorithm

A New Spatial Domain Filter for Impulse Noise Removal with Improved Accuracy Based on Multiple Conditions

Numerous filtering methods are proposed for Impulse noise removal, it is an important task in the field of image restoration. The familiar spatial domain algorithm to remove impulse noise is Standard Median Filter (SMF). Most of the existing algorithms are based on median filtering and recent algorithms are Modified Hybrid Median Filter (MHMF) and New Modified Hybrid Median Filter (NMHMF). These two are worked up to 20% noise density. In this paper proposed a new` algorithm for impulse noise removal above 20% noise density conditions with different samples of images. The implementation of proposed method compares with known existing methods by comparing Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

A random-valued impulse noise removal algorithm via just noticeable difference threshold detector and weighted variation method

The paper presents a random-valued impulse noise (RVIN) removal algorithm by using distinct impulse noise detection and correction stages. In the noise detection phase, the local luminance adaption of an image is used to detect the obvious noise pixels firstly, and the just noticeable difference (JND) is introduced to detect the noise pixels hiding in image details afterward. In the noise candidate correction phase, the obvious noise pixels, almost distributing in the smooth region, are restored by a weighted mean filter. For the noise candidates hiding in image details, we adopt a weighted detail-preserving variation method to restore them. Since human visual perception and detail-preserving are taken into account during the course of the noises detection and restoration, our method outperforms some existing methods, both in visual and quantitative measurements.

Iterative scheme-inspired network for impulse noise removal

This paper presents a supervised data-driven algorithm for impulse noise removal via iterative scheme-inspired network (IIN). IIN is defined over a data flow graph, which is derived from the iterative procedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing the L1-guided variational model. In the training phase, the L1-minimization is reformulated into an augmented Lagrangian scheme through adding a new auxiliary variable. In the testing phase, it has computational overhead similar to ADMM but uses optimized parameters learned from the training data for restoration task. Experimental results demonstrate that the newly proposed method can obtain very significantly superior performance than current state-of-the-art variational and dictionary learning-based approaches for salt-and-pepper noise removal.

A sporadic decomposition of Hankel structured matrix in logarithmic and wavelet domain for impulse noise removal

Noise removal from the color images is the most significant and challenging task in image processing. Among different conventional filter methods, a robust Annihilating filter-based Low-rank Hankel matrix (r-ALOHA) approach was proposed as an impulse noise removal algorithm that uses the sparse and low-rank decomposition of a Hankel structured matrix to decompose the sparse impulse noise components from an original image. However, in this algorithm, the patch image was considered as it was sparse in the Fourier domain only. It requires an analysis of noise removal performance by considering the other transform domains. Hence in this article, the r-ALOHA can be extended into other transform domains such as log and exponential. In the log and exponential domain, the logarithmic and exponential functions are used for modeling the multiplicative noise model. But, this model is used only for positive outcomes. Therefore, wavelet transform domain is applied to the noise model that localizes an image pixel in both frequency and time domain simultaneously. Moreover, it separates the most vital information in a given image. Thus, it is feasible for obtaining a better approximation of the considered function using few coefficients. Finally, the experimental results show the performance effectiveness of the proposed algorithm.

Real-time impulse noise removal

An adaptive interpolation-based impulse noise removal (AIBINR) algorithm is proposed to remove impulse noise from color and gray-scale images in real time. AIBINR works fast and has no need for parameter tuning to remove fixed-valued impulse noise. A GPU application has been developed to demonstrate the speed and inherent parallelization capabilities of the proposed method. Using the high-speed implementation, we have shown that AIBINR can denoise color images fast enough to be used in real-time video denoising, while having comparable denoising performance when compared to the state-of-the-art methods without any modification to its parameters.

Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal.

Recently, the annihilating filter-based low-rank Hankel matrix (ALOHA) approach was proposed as a powerful image inpainting method. Based on the observation that smoothness or textures within an image patch correspond to sparse spectral components in the frequency domain, ALOHA exploits the existence of annihilating filters and the associated rank-deficient Hankel matrices in an image domain to estimate any missing pixels. By extending this idea, we propose a novel impulse-noise removal algorithm that uses the sparse and low-rank decomposition of a Hankel structured matrix. This method, referred to as the robust ALOHA, is based on the observation that an image corrupted with the impulse noise has intact pixels; consequently, the impulse noise can be modeled as sparse components, whereas the underlying image can still be modeled using a low-rank Hankel structured matrix. To solve the sparse and low-rank matrix decomposition problem, we propose an alternating direction method of multiplier approach, with initial factorized matrices coming from a low-rank matrix-fitting algorithm. To adapt local image statistics that have distinct spectral distributions, the robust ALOHA is applied in a patch-by-patch manner. Experimental results from impulse noise for both single-channel and multichannel color images demonstrate that the robust ALOHA is superior to existing approaches, especially during the reconstruction of complex texture patterns.

An effective directional interpolation- and inpainting-based algorithm for removing impulse noise

In this paper, an effective directional interpolation- and inpainting-based impulse noise removal algorithm is proposed. Firstly, each noisy pixel is classified to either the low-density noise or the middle/high density noise. Secondly, a directional interpolation-based noise removal procedure is proposed to denoise the low-density noise. Thirdly, an inpainting-based noise removal procedure is proposed to denoise the middle/high-density noise. Based on ten typical test images, each image with noise level ranging from 30 to 90%, the experimental results demonstrate that in terms of peak-signal-to-noise-ratio (PSNR), structural similarity index (SSIM), and visual effect, the proposed algorithm has the best quality performance when compared with six state-of-the-art noise removal algorithms.

A Novel Image Impulse Noise Removal Algorithm Optimized for Hardware Accelerators

Images are often corrupted with noise during the image acquisition and transmission stage. Here, we propose a novel approach for the reduction of random-valued impulse noise in images and its hardware implementation on various state-of-the-art FPGAs. The presented algorithm consists of two stages in which the first stage detects whether pixels have been corrupted by impulse noise and the second stage performs a filtering operation on the detected noisy pixels. The human visual system is sensitive to the presence of edges in any image therefore the filtering stage consists of an edge preserving median filter which performs the filtering operation while preserving the underlying fine image features. Experimentally, it has been found that the proposed scheme yields a better Peak Signal-to-Noise Ratio (PSNR) compared to other existing median-based impulse noise filtering schemes. The algorithm is implemented using the high-level synthesis tool PARO as a highly parallel and deeply pipelined hardware design that simultaneously exploits loop level as well as instruction level parallelism with a very short latency of only few milliseconds for 16 bit images of size 512 × 512 pixels.

Impulse Noise Removal Using Adaptive Radial Basis Function Interpolation

A novel adaptive radial basis function interpolation-based impulse noise removal algorithm is introduced in this manuscript. This approach consists of two stages: noisy pixel detection and correction. In former step, the noise-affected pixels in an image are detected, and in the latter step, the noisy pixels are restored by adaptive radial basis function-based interpolation scheme. The radial basis function interpolation scheme is used to estimate the unknown noisy pixel value from the noise-free known neighboring pixel values. For both noisy pixel detection and correction, a center sliding window is considered at each pixel location. The proposed approach is experimented on some benchmark data sets, and its performance is evaluated using five performance evaluation measures: PSNR, MSSIM, IEF, correlation factor, and NSER on different test images by comparing it against sixteen different state-of-the-art techniques. It is found that the proposed approach gives better results than the sixteen different state-of-the-art techniques.

A New Improved Multistage Eight Directional Median Filter

Background/Objectives: To propose a new improved multistage multi directional median filtering algorithm for impulse noise removal. Methods/Statistical Analysis: Calculate standard deviation of the 5 pixels in each direction giving extra weight to the direction in which the standard deviation is smallest, the weighted median is computed and the noisy pixel is replaced with this median value then move window throughout the image and iterate the process. Findings: The experimental results show that the proposed filter can provide excellent performance of suppressing impulse noise in all situations. Improvement/Application: The paper offers a new improved multistage eight directional median filtering algorithm for impulse noise removal from digital images.

Edge-preserving regularized filter with visual perception outlier measurement

In this paper, we propose an impulse noise removal algorithm, which can realize the elimination of the impulse noise in an image effectively even though these noises are on image edges or details. The algorithm introduces a statistic, visual perception outlier measurement, to identify the impulse noise in an image. Once the noises are identified, noise candidates are restored by a nonlocal edge-preserving regularized filter. Extensive experimental results indicate that our algorithm provides a significant improvement over many other existing techniques.

Impulse noise filter via spatial global outlier measurement

We propose an impulse noise removal algorithm which can effectively realize the elimination of the impulse noise in an image even though these noise pixels are on image edges or details. The algorithm introduces a statistic, spatial global outlier measurement to identify the impulse noise in an image. Once the noise pixels are identified, noise candidates are restored by a nonlocal edge-preserving regularized filter. Extensive experimental results indicate that our algorithm provides a significant improvement over many other existing techniques.

Super Resolution Reconstruction in Mixed Noise Environment

A hybrid Super Resolution (SR) algorithm is proposed to deal with the Low Resolution (LR) images degraded by Mixed (Gaussian + Impulse) noise. The algorithm adaptively estimates and removes the impulse noise from the input LR images based on edge, geometrical & size characteristics. The fuzzy based impulse noise removal algorithm is along with adaptive sharpening filter based SR using steering kernel regression are used to obtain a HR image. The experimental results confirm the efficacy of the algorithm for different types of images at various noise densities. General Terms Super Resolution, Mixed Noise, Surveillance applications,

Decision Based Median Filter algorithm using Resource Optimized FPGA to Extract Impulse Noise

Median filter is a non-linear filter used in image processing for impulse noise removal. It finds its typical application in the situations where edges are to be preserved for higher level operations like segmentation, object recognition etc. This paper presents an accurate and efficient noise detection and filtering algorithm for impulse noise removal. The algorithm includes two stages: noise detection followed by noise filtering. The proposed algorithm replaces the noisy pixel by using median value when other pixel values, 0’s or 255’s are present in the selected window and when all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. Similarly algorithm checks for five different conditions to preserve image details, object boundary in high level of noise densities. This median filter was designed, simulated and synthesized on the Xilinx family of FPGAs (XC3S500E of Spartan-3E). The VHDL was used to design the above 2-D median filter using ISE (Xilinx) tool & tested & compared for different grayscale images.

A Modified Iterative Alternating Direction Minimization Algorithm for Impulse Noise Removal in Images

Images are often corrupted by impulse noise. In this paper, an alternating direction minimization with continuation algorithm is modified and iteratively used to remove the impulse noise in images by exploring its self-similarity. A patch-based nonlocal operator and sparse representation are married in thel1-l1optimization model to be solved. Simulation results demonstrate that the proposed algorithm outperforms typical denoising methods in terms of preserving edges and textures for both salt-and-pepper noise and random-valued impulse noise. It can be also applied to suppress impulse noise-like artifacts in real mural images.

A Novel Edge Detection Algorithm of Color Image

This paper proposes a novel algorithm of edge detection using LUV color space. Firstly, according to peer group filtering (PGF), a nonlinear algorithm for image smoothing and impulse noise removal in color image is used. Secondly, color image edges in an image are obtained automatically by combining an improved isotropic edge detector and a fast entropy threshold technique. Thirdly, according to color distance between the pixel and its eight neighbor-pixels, color image edges can further be detected. Finally, the experiment demonstrates the outcome of proposed algorithm in color image edge detection.

Interpolation‐based impulse noise removal

Interpolation-Based Impulse Noise Removal (IBINR), a fast and simple algorithm is proposed to remove fixed valued impulse noise in this study. The proposed method removes all noisy pixels from the image and determines their values using non-linear interpolation. Compared with the state-of-the-art noise removal algorithms, IBINR has the highest or comparable performance in terms of photographic image denoising power and resource efficiency: it runs in shorter amount of time and does not have any significant additional memory requirements due to the fact that costly sorting operations are avoided.

Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies

In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well-known boundary discriminative noise detection method have been made. First, rather than using any sorting algorithm, we determine the local median values from manipulated local histograms. This solution makes the execution of the algorithm faster. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach in noise cancellation stage, our approach uses iterative approach, which has better local content preservation ability. Broad impulse noise model has been employed in this experiment. Based on the evaluations from root mean square error, false positive detection rate, false negative detection rate, mean structure similarity index, processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the-art median filtering techniques.

Edge-Preserving Regularized Filter with Spatial Local Outlier Measure and Q-Estimate

This paper presents an efficient random-valued impulse noise removal algorithm. The filtering process contains two phases: a detection phase followed by a filtering phase. In the detection phase, the proposed method uses the novel image statistics, the spatial local outlier measure (SLOM) and the Q-estimate, to identify impulses in a corrupted image. When the noise pixels are identified, their values are restored by an edge-preserving regularized method in the filtering phase. Extensive experimental results show that our filter provides a significant improvement over many other existing techniques.