Denoising Filter Research Articles

Low power and low area VLSI implementation of vedic design FIR filter for ECG signal de-noising

In recent years, Finite Impulse Response (FIR) filter plays a major role in signal processing applications. Earlier many research papers are described the different types of FIR filter design. But, none of the paper explained about signal denoising application with an effective multiplier design. In this paper, Vedic Design - Carry Lookahead Adder FIR filter architecture is introduced to perform the FIR filter operation with Electro Cardiogram (ECG) signal de-noising application. By usingthe MATLAB program, the input ECG signal is read and Additive White Gaussian Noise (AWGN) is added to the input signal. The denoising process is implemented in Verilog and the obtained output is written in text files. For de-noising the signal, the binary text values are read in MATLAB. With the help of Verilog code, FPGA performance (LUT, flip flop, slices, and frequency) and ASIC performance (area, power, and delay) are evaluated. For ASIC implementation, 180 nm and 45 nm technology are used and for FPGA implementation Virtex-4, Virtex-5, and Virtex-6 devices are used to evaluate the performance. The Mean Square Error (MSE), Bit Error Rate (BER), and Signal to Noise Ratio (SNR) performance are calculated from the de-noised signal. In 180 nm technology, 42.39% of the area, 29.53% of delay, 43.89% of APP, 70.41% of ADP reduced in VD-CLA-FIR. In 45 nm technology, 13.2% of the area, 32.25% of the delay, 24.37% of APP, and 39.02% of ADP reduced in VD-CLA-FIR method compared to the conventional methods.

3D MR Image Denoising using higher Order Kernel Regression

Noise removal from images is among the challenging processes for researches. Image denoising is a crucial step to improve 3D image conspicuity and to enhance the performance of all the processing needs of quantitative image analysis. Magnetic Resonance (MR) imaging has an increasing importance in the field of medical diagnosis. MR 3D image de-noising has two features (i) tri-dimensional structure of images and (ii) the nature of the noise, which are Rician & Gaussian. Kernel regression is one of 3D non-parametric noise level estimation technique which is effective than other denoising experimental filters. The proposed Fourth order Kernel Regression (FKR) algorithm builds an efficient and robust estimator and improves the accuracy of noise and it further improves the finer estimations of pixel value and its gradients. Experimental results demonstrate positively by achieving better performance, with respect to other de-noising filters.

Unified Noise Reduction using Adaptive Radial Basis Function

The images captured by SAR and sonar are blurred and corrupted more by speckle noise and also other types of noise like Gaussian noise and salt & pepper noise. Denoising all types of noises to get perfect image is a vital challenge, earlier works on the same mode addressed with one filter for one noise, there is no one common or unified filter which can denoise all types of noise. Therefore in this paper, we have designed a filter which not only removes speckle noise, but also combination of other noises. Here IUNR (Intelligent Unified Noise Reduction) algorithm is proposed which is based on neural network called adaptive radial basis function acts as a unified filter for Denoising. Proposed method needs a single noisy image to train the adaptive radial basis function neural network to learn the correction of the noisy image. The Gaussian kernel function is applied to reconstruct the local disturbance appeared because of the noise. The proposed adaptive radial basis function network is compared with the fixed form which has fixed spread and the center value of kernel function. This method can correct the image suffered from different varieties of noises like speckle noise, salt & pepper noise and Gaussian noise separately or combination of noise. Various standard test images are considered for test purpose with different levels of noise density and performance of proposed algorithm is compared with adaptive wiener filter.

Fast and robust Fourier domain ranging for light field

A light-field camera combines optics and computation to provide the ability to perform ranging. Many methods and algorithms, which are time-consuming and noise-sensitive, have been proposed for per-pixel depth estimation. We present a Fourier domain ranging method for light fields (LFs). Instead of estimating the depth for every pixel, we attempt to detect the depths of the different planes at which objects are located. The method is somewhat like using the energy in the focal stack, but it is carried out in a more efficient way. Our method has the advantages of speediness and robustness compared with traditional per-pixel depth estimation methods. In addition to the ranging algorithm, we also demonstrate the idea and application of a region adaptive denoising filter, in which the depth parameters are tuned by the proposed method. We include results for synthetic LFs, the Stanford LF archives, and LFs captured with a Lytro camera, exploiting the algorithm complexity, accuracy, depth resolution, and noise performance of our method. Our method uses less computation and is more robust than per-pixel depth estimation, making it appropriate for many applications, such as autorefocusing and autodenoising.

Enhanced Energy Detectors Utilizing Wavelet and RLS De-Noising Filters in Cognitive Radio

Enhanced Energy Detectors Utilizing Wavelet and RLS De-Noising Filters in Cognitive Radio

Development of a Robust Carrier Synchronizer for High Dynamic and Low Signal to Noise Ratio Signals in GPS Receivers

In this paper, a robust carrier recovery loop for high dynamic and weak signals in GPS receivers is proposed. To achieve optimal tracking performance at synchronization loops, different methods are investigated. Since phase jitter sources are identified at the receiver, suitable controller coefficients are calculated and used in carrier and timing recovery loops. This improvement induces higher speed and lower variance of jitter in acquisition and tracking modes, respectively. Furthermore, this simple design can obviate the need of large digital filters or complex circuits such as wavelet de-noising filters in the loop. To extend the linear range of the phase estimator, phase unwrapper unit is used which can improve loop tracking ability and resolve Kalman filter difficulties. To compensate the cycle-slip phenomenon, a novel recursive algorithm is introduced which causes better performance for low CNR signals and high dynamic environments. Simulation results show the effectiveness of the proposed architecture for different Doppler rates and CNRs. Moreover, the results indicate good matching between fixed-point FPGA implementation and floating-point simulation.

Research on FPGA filtering method of Gearbox fault signal

Gearboxes are an important part of marine machinery and equipment such as steam turbines and generators. Under normal circumstances, the performance of the gearbox directly affects whether the relevant mechanical equipment can normally work, so it is very important to detect and eliminate the gearbox failure. The gearbox vibration signal contains a wealth of working status information, and the use of gearbox vibration signal diagnosis is a common method. Since the vibration signal contains strong noise, denoising is the primary task of fault feature extraction. Aiming at this practical problem, in this paper, a denoising filter based on FPGA environment is designed. This filter is used to process the simulated gear fault signal with a signal-to-noise ratio (SNR) of 0.1206. By comparing the time domain diagram and frequency domain diagram before and after processing, the feasibility of using FPGA to realize the mechanical vibration signal processing is verified. This is of great practical significance to the miniaturization and real-time of the mechanical fault diagnosis system.

A Bayesian approach based on Kalman filter frameworks for bullet identification

When a bullet is fired from a barrel, random imperfections in the interior surface of the barrel imprint 3-D micro structures on the bullet surface that are seen as striations. Despite being random and non-stationary in nature, these striations are known to be consistently reproduced in a unique pattern on every bullet. This is a key idea in bullet identification. Common procedures in the field of automatic bullet identification include extraction of a feature profile from bullet image, profile smoothing and comparison of profiles using normalized cross correlation. Since the cross correlation based comparison is susceptible to high-frequency noise and nonlinear baseline drift, profile smoothing is a critical step in bullet identification. In previous work, we considered bullet images as nonlinear non-stationary processes and applied ensemble empirical mode decomposition (EEMD) as a preprocessing algorithm for smoothing and feature extraction. Using EEMD, each bullet average profile was decomposed into several scales known as intrinsic mode functions (IMFs). By choosing an appropriate range of scales, the resultant smoothed profile contained less high-frequency noise and no nonlinear baseline drift. But the procedure of choosing the proper number of IMFs to reduce the high-frequency noise effect was manual. This poses a problem in comparison of bullets whose images contained less or more noise in comparison to others because their useful information may be present in the corresponding discarded IMFs. Moreover, another problem arises when the bullet type changes. In this case manual inspection is needed once more to figure out which range of IMFs contain less high-frequency noise for this particular type of bullet. In this paper, we propose a novel combination of EEMD and Bayesian Kalman filter to solve these problems. First the bullet images are rotated using Radon transform. The rotated images are averaged column-wise to acquire averaged 1-D profiles. The nonlinear baseline drifts of averaged profiles are removed using EEMD algorithm. The profiles are then processed by a Kalman filter that is designed to automatically and optimally reduce the effect of high-frequency noise. Using Expectation Maximization (EM) technique, for each averaged profile, the parameters of Kalman filter are reconfigured to optimally suppress the high-frequency noise in each averaged profile. This work is the first effort that practically implements the Kalman filter for optimal denoising of firearm image profiles. In addition, we believe that Euclidean distance metric can help the normalized cross-correlation based comparison. Therefore, in this paper, we propose a comparison metric that is invariant to start and endpoints of firearm image profiles. This metric combines the prized properties of both Euclidean and normalized cross-correlation metrics in order to improve identification results. The proposed algorithm was evaluated on a database containing 180 2-D gray-scale images acquired from bullets fired from different AK-47 assault rifles. Although the proposed method needs more calculations in comparison to conventional methods, the experiments showed that it attained better results compared with the conventional methods and the previous method based on EMD in the field of automatic bullet identification.

An Adaptive TLS-ESPRIT Algorithm Based on an S-G Filter for Analysis of Low Frequency Oscillation in Wide Area Measurement Systems

In this paper, a two-stage mode identification algorithm including preprocessing and identification steps is introduced to solve the problems of measuring noise and inaccurate mode identification in the analysis of low-frequency oscillation (LFO) in a wide area measurement systems (WAMSs). S-G filter de-noising is utilized for the preprocessing. An adaptive total least squares-estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) algorithm with the order setting of the singular value accumulation percentage adjacency increment ratio (SVAPAIR) is utilized for the identifying stage. The S-G filter is used to mitigate the measuring of LFO noise of the system. Furthermore, the SVAPAIR is adopted to achieve an adaptive and precise determination of the LFO order, and then LFO parameters are extracted by the adaptive TLS-ESPRIT algorithm. The proposed algorithm has been tested and verified using the IEEE four-generator two-area system and the actual measured data of the LFO accident in the North American power grid. The simulation and experimental result showed that the proposed algorithm has better adaptability, anti-noise characteristic, and robustness compared to those of the conventional Prony, matrix pencil (MP), and TLS-ESPRIT algorithms.

Adaptive Wavelet Filter With Edge Compensation for Remote Sensing Image Denoising

Advanced Geostationary Radiation Imager (AGRI) is one of the main payloads of the second-generation geostationary orbit meteorological satellite, FengYun-4A. Typically, the existence of variable stripe noise in the water vapor band remote sensing images of the AGRI greatly affects many applications, such as cloud detection, especially as one full disk image is separated into ten sub-images for transforming as soon as possible, so the denoising algorithm, which can reduce variable stripe noise and is adaptive to process using sub-images, must be built. In this paper, we propose an adaptive wavelet filter for image denoising. This approach introduces a new parameter termed weight sum variance of digital number probability (WSVODP), which is used to indicate the appropriate wavelet filter coefficients. WSVODP is only sensitive to the difference of observation targets of different sensors. Thus, our approach can learn appropriate wavelet filter coefficients fast and exactly. We built a real-world remote sensing image dataset from AGRI on FengYun-4A, and the experimental results on this dataset show that the proposed approach could effectively reduce the variable stripe noise from different observation targets. At the same time, an edge compensation method, which is fitted to the scanning model of the AGRI, is suggested to avoid ringing artifacts. Many applications, such as cloud detection with denoised images, show very good results. The proposed approach reduces the stripe noise adaptation, so the result is very steady even if the stripe noise varies with different targets, and edge compensation ensures that there are no obvious ringing artifacts in the full disk image joined by the ten sub-images.

Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image

In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image, a denoising method of medical image based on discrete wavelet transform (DWT) and modified median filter for medical image coupling denoising is proposed. The method is composed of four modules: image acquisition, image storage, image processing and image reconstruction. Image acquisition gets the medical image that contains Gaussian noise and impulse noise. Image storage includes the preservation of data and parameters of the original image and processed image. In the third module, the medical image is decomposed as four sub bands (LL, HL, LH, HH) by wavelet decomposition, where LL is low frequency, LH, HL, HH are respective for horizontal, vertical and in the diagonal line high frequency component. Using improved wavelet threshold to process high frequency coefficients and retain low frequency coefficients, the modified median filtering is performed on three high frequency sub bands after wavelet threshold processing. The last module is image reconstruction,which means getting the image after denoising by wavelet reconstruction. The advantage of this method is combining the advantages of median filter and wavelet to make the denoising effect better, not a simple combination of the two previous methods. With DWT and improved median filter coefficients coupling denoising, it is highly practical for high-precision medical images containing complex noises. The experimental results of proposed algorithm are compared with the results of median filter, wavelet transform, contourlet and DT-CWT, etc. According to visual evaluation index PSNR and SNR and Canny edge detection, in low noise images, PSNR and SNR increase by 10%–15%; in high noise images, PSNR and SNR increase by 2%–6%. The experimental results of the proposed algorithm achieved better acceptable results compared with other methods, which provides an important method for the diagnosis of medical condition.

A locally adaptive edge preserving filter for denoising of low dose CT using multi-level fuzzy reasoning concept

To reduce the radiation exposure, low dose CT (LDCT) imaging has been particularly used in modern medical practice. The fundamental difficulty for LDCT lies in its heavy noise pollution in the projection data which leads to the deterioration of the image quality and diagnostic accuracy. In this study, a novel two-stage locally adaptive edge preserving filter based on multi-level fuzzy reasoning (LAEPMLFR) concept is proposed as an image space denoising method for LDCT images. The first stage of structured pixel region employs multi-level fuzzy reasoning to handle uncertainty present in the local information introduced by noise. The second stage employs a Gaussian filter to smooth both structured and non-structured pixel region in order to retain the low frequency information of the noisy image. Comparing with traditional denoising methods, the proposed method demonstrated noticeable improvement on noise reduction while maintaining the image contrast and edge details of LDCT images.

A locally adaptive edge preserving filter for denoising of low dose CT using multi-level fuzzy reasoning concept

A locally adaptive edge preserving filter for denoising of low dose CT using multi-level fuzzy reasoning concept

Adaptive multi-threshold based de-noising filter for medical image applications

Medical image processing is the emerging research area and many researchers contributed to medical image processing by proposing new techniques for medical image enhancement and abnormality detection. Interpretation of medical images is a challenging problem because of the unavoidable noise produced by the medical imaging devices and interference. In this work, a new framework is proposed for noise detection and reduction. This framework comprises two phases. First phase is the noise detection phase which is performed using the newly proposed adaptive multi-threshold scheme (AMT). In second phase, modification of noisy pixel is done using edge preserving median filter (EPM), which conserves the edge component and controls the blurring effect with preservation of fine details of interior region. The proposed work is tested with benchmark images and few medical images. It produces promising result and the results are compared with existing two-stage noise reduction techniques. Popular performance metrics such PSNR and SSIM are used for evaluation. Quantitative analysis and experimental results demonstrate that the proposed method is more efficient and suitable for medical image pre-processing.

Dual Ascent Based Median Filter for Image Restoration

In the spetial domain, the non-linear filter effectively reduces the noise in an image but at the simalteniously, they blur the edge of images. Finally based on the study effective algorithm will be developed and used for effective denoising and image restoration. Proposed median filter for the restoration of corrupted image by noise and blurred and thereby to improve the image quality. The performance of denoised image quality and restoration technique has been judge by comparing the result of proposed technique with the existing denoising filters. The comparison result shows a high value of PSNR.

Adaptive multi-threshold based de-noising filter for medical image applications

Medical image processing is the emerging research area and many researchers contributed to medical image processing by proposing new techniques for medical image enhancement and abnormality detection. Interpretation of medical images is a challenging problem because of the unavoidable noise produced by the medical imaging devices and interference. In this work, a new framework is proposed for noise detection and reduction. This framework comprises two phases. First phase is the noise detection phase which is performed using the newly proposed adaptive multi-threshold scheme (AMT). In second phase, modification of noisy pixel is done using edge preserving median filter (EPM), which conserves the edge component and controls the blurring effect with preservation of fine details of interior region. The proposed work is tested with benchmark images and few medical images. It produces promising result and the results are compared with existing two-stage noise reduction techniques. Popular performance metrics such PSNR and SSIM are used for evaluation. Quantitative analysis and experimental results demonstrate that the proposed method is more efficient and suitable for medical image pre-processing.

Shearlet and guided filter based despeckling method for medical ultrasound images

Ultrasound diagnostic techniques are widely used in medical clinical diagnostics. However, the presence of speckle noise in the ultrasound imaging process reduces the image quality and make inconveniences for the clinical diagnosis of the physician. Therefore, to decline the influence of speckle noise has important significance in medical ultrasound image diagnosis. In order to solve the problem of speckle noise, this paper proposes a novel de-speckle method for medical ultrasound imaging, which is based on nonsubsampled shearlet transformation (NSST) and guided filter. Firstly, the nonsubsampled Laplacian pyramid filtering is used to decompose the noisy image, and thus the image is decomposed into high frequency subbands and low frequency subbands. Under the direction of non-sampling filter bank, we obtain a high-frequency subband multi-directional decomposition; Secondly, based on the threshold function and the correlation of the shearlet coefficients in the transformation domain, an improved threshold shrinkage algorithm is proposed to perform the threshold shrinkage processing on the shearlet coefficients of the high frequency subbands. Finally, the low frequency subbands in the transformation domain are processed by the guided filter, and the denoised ultrasonic image is obtained by the inverse transformation of the shearlet. In order to verify the effectiveness of the proposed method, experiments are conducted, and the results were compared with those of other existing denoising filter. Showing that the proposed method has a strong de-noising performances and maintain image details.

Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection

This article investigates the potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection in comparison to conventional TanDEM-X data, i.e. image pairs acquired in repeat-pass or bistatic mode. For this task, an unsupervised coastline detection procedure based on scale-space representations and K-medians clustering as well as morphological image post-processing is proposed. Since this procedure exploits a clear discriminability of “dark” and “bright” appearances of water and land surfaces, respectively, in both SAR amplitude and coherence imagery, TanDEM-X InSAR data acquired in pursuit monostatic mode is expected to provide a promising benefit. In addition, we investigate the benefit introduced by a utilization of a non-local InSAR filter for amplitude denoising and coherence estimation instead of a conventional box-car filter. Experiments carried out on real TanDEM-X pursuit monostatic data confirm our expectations and illustrate the advantage of the employed data configuration over conventional TanDEM-X products for automatic coastline detection.

Fast Denoising Filter for MRI using Parallel Approach

Real time medical image processing is necessary in the domain of remote medical care, diagnostics and surgery. To provide fast MRI diagnostics especially for neuro imaging, the research work proposes CUDA GPU based fast denoising filter with a parallel approach. Bilateral filter is the most suitable candidate for denoising, as it has unique ability to retain contours of soft tissue structures of the brain. The work proposes improvised memory optimization techniques for the GPU implementation to achieve superior performance in terms of speed up when compared with existing work. For a 64Megapixel brain MR image, shared memory approach gives speed up of 256.5 while texture memory usage with tiling approach stands the next in speedup with 42.16 over its CPU counterpart. The results indicate that in spite of increase in image size, the execution time of the filter does not increase beyond 500msec keeping the performance real time.

A comprehensive survey on impulse and Gaussian denoising filters for digital images

Abstract This review article provides a comprehensive survey on state-of-the-art impulse and Gaussian denoising filters applied to images and summarizes the progress that has been made over the years in all applications involving image processing. The random noise model in this survey is assumed to be comprised of impulse (salt and pepper) and Gaussian noise. Different noise models are addressed, and different types of denoising filters are studied in terms of their performance on digital images and in their various practical implications and domains of application. A comprehensive comparison is performed to cover all the denoising methods in details and the results they yield. With this extensive review, researchers in image processing will be able to ascertain which of these denoising methods will be best applicable to their research needs and the application domain where such methods are contemplated for implementation.