Image Processing for Denoising Using Composite Adaptive Filtering Methods Based on RMSE

As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images contaminated by unknown noise has gradually become one of the research focuses. In order to achieve blind denoising and separation to restore images, this paper proposes a method for image processing based on Root Mean Square Error (RMSE) by integrating multiple filtering methods for denoising. This method includes Wavelet Filtering, Gaussian Filtering, Median Filtering, Mean Filtering, Bilateral Filtering, Adaptive Bandpass Filtering, Non-local Means Filtering and Regularization Denoising suitable for different types of noise. We can apply this method to denoise images contaminated by blind noise sources and evaluate the denoising effects using RMSE. The smaller the RMSE, the better the denoising effect. The optimal denoising result is selected through comprehensively comparing the RMSE values of all methods. Experimental results demonstrate that the proposed method effectively denoises and restores images contaminated by blind noise sources.

BeFOI: A Novel Method Based on Conditional Diffusion Model for Medical Image Denoising

The progress in medical imaging technology highlights the importance of image quality for effective diagnosis and treatment.Yet,noise during capture and transmission can compromise image accuracy and reliability,complicating clinical decisions.The rising interest in diffusion models has led to their exploration of denoising images.We present Be-FOI(Better Fluoro Images),a weakly supervised model that uses cine images to denoise fluoroscopic images,both DR types.Trained through precise noise estimation and simulation,BeFOI employs Markov chains to denoise using only the fluoroscopic image as guidance.Our tests show that BeFOI outperforms other methods,reducing noise and enhancing clar-ity and diagnostic utility,making it an effective post-processing tool for medical images.

Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising

Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are exposed.Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients;on the other hand,decreasing it by using a Low-Dose CT(LDCT)image may cause more noise and increased artifacts,which can compromise the diagnosis.So,image reconstruction from LDCT image data is necessary to improve radiologists’judgment and confidence.This study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network(WGAN).They were incorporated with different loss functions,including Visual Geometry Group(VGG),Structural Similarity Loss(SSIM),and Structurally Sensitive Loss(SSL),to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss functions.Furthermore,experiments have been conducted on the Graphical Processing Unit(GPU)and Central Processing Unit(CPU)to compare the performance of the proposed models.The results demonstrated that images from the proposed WGAN-SSIM,WGAN-VGG-SSIM,and WGAN-VGG-SSL were denoised better than those from state-of-the-art models(WGAN,WGAN-VGG,and SMGAN)and converged to a stable equilibrium compared with WGAN and WGAN-VGG.The proposed models are effective in reducing noise,suppressing artifacts,and maintaining informative structure and texture details,especially WGAN-VGG-SSL which achieved a high peak-signalto-noise ratio(PNSR)on both GPU(26.1336)and CPU(25.8270).The average accuracy of WGAN-VGG-SSL outperformed that of the state-ofthe-art methods by 1 percent.Experiments prove that theWGAN-VGG-SSL is more stable than the other models on both GPU and CPU.

Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

In signal processing and communication systems,digital filters are widely employed.In some circumstances,the reliability of those systems is crucial,necessitating the use of fault tolerant filter implementations.Many strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the filters.As technology advances,more complicated systems with several filters become possible.Some of the filters in those complicated systems frequently function in parallel,for example,by applying the same filter to various input signals.Recently,a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was given.Many fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the years.The primary idea is to use structured authentication scan chains to study the internal states of finite impulse response(FIR)components in order to detect and recover the exact state of faulty modules through the state of non-faulty modules.Finally,a simple solution of Double modular redundancy(DMR)based fault tolerance was developed that takes advantage of the availability of parallel filters for image denoising.This approach is expanded in this short to display how parallel filters can be protected using error correction codes(ECCs)in which each filter is comparable to a bit in a standard ECC.“Advanced error recovery for parallel systems,”the suggested technique,can find and eliminate hidden defects in FIR modules,and also restore the system from multiple failures impacting two FIR modules.From the implementation,Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of implementation.Faults were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

SAR image denoising based on wavelet-fractal analysis

Wavelet-fractal based SAR （synthetic aperture radar） image processing is one of the advanced technologies in image processing. The main concept of analysis is that after wavelet transformation, multifractal spectrum of the signal is different from that of noise. This difference is used to alleviate the noise produced by SAR image.The method to denoise SAR image using the process based on wavelet-fractai analysis is discussed in detail. Essentially, the present method focuses on adjusting the Hoelder exponent α of multifractal spectrum. After simulation, α should be adjusted to 1.72-1.73. The more the value of α exceeds 1.73, the less distinctive the edges of SAR image become. According to the authors denoising is optimal at α=1.72-1.73. In other words, when α =1.72-1.73, a smooth and denoised SAR image is produced.

Improved Non-Local Means Algorithm for Image Denoising

Image denoising technology is one of the forelands in the field of computer graphic and computer vision. Non-local means method is one of the great performing methods which arouse tremendous research. In this paper, an improved weighted non-local means algorithm for image denoising is proposed. The non-local means denoising method replaces each pixel by the weighted average of pixels with the surrounding neighborhoods. The proposed method evaluates on testing images with various levels noise. Experimental results show that the algorithm improves the denoising performance.

Image denoising algorithm of refuge chamber by combining wavelet transform and bilateral filtering

In order to preferably identify infrared image of refuge chamber, reduce image noises of refuge chamber and retain more image details, we propose the method of combining two-dimensional discrete wavelet transform and bilateral denoising. First, the wavelet transform is adopted to decompose the image of refuge chamber, of which low frequency component remains unchanged. Then, three high-frequency components are treated by bilateral filtering, and the image is reconstructed. The result shows that the combination of bilateral filtering and wavelet transform for image denoising can better retain the details which are included in the image, while providing better visual effect. This is superior to using either bilateral filtering or wavelet transform alone. It is useful for perfecting emergency refuge system of coal mines.

Improved Weight Function for Nonlocal Means Image Denoising

The nonlocal means( NLM) has been widely used in image processing. In this paper,we introduce a modified weight function for NLM denoising, which will compute the nonlocal similarities among the pre-processing pixel patches instead of the commonly used similarity measure based on noisy observations. By the law of large number,the norm for the pre-processing pixel patches is closer to the norm of the original clean pixel patches,so the proposed weight functions are more optimized and the selected similar patches are more accurate. Experimental results indicate the proposed algorithm achieves better restored results compared to the classical NLM's method.

Fractional-order Sparse Representation for Image Denoising

Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation(FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each cluster.Then a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.

Image Denoising with Adaptive Weighted Graph Filtering

Graph filtering,which is founded on the theory of graph signal processing,is proved as a useful tool for image denoising.Most graph filtering methods focus on learning an ideal lowpass filter to remove noise,where clean images are restored from noisy ones by retaining the image components in low graph frequency bands.However,this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective.To address this issue,we propose an adaptive weighted graph filtering(AWGF)method to replace the design of traditional ideal lowpass filter.In detail,we reassess the existing low-rank denoising method with adaptive regularizer learning(ARLLR)from the view of graph filtering.A shrinkage approach subsequently is presented on the graph frequency domain,where the components of noisy image are adaptively decreased in each band by calculating their component significances.As a result,it makes the proposed graph filtering more explainable and suitable for denoising.Meanwhile,we demonstrate a graph filter under the constraint of subspace representation is employed in the ARLLR method.Therefore,ARLLR can be treated as a special form of graph filtering.It not only enriches the theory of graph filtering,but also builds a bridge from the low-rank methods to the graph filtering methods.In the experiments,we perform the AWGF method with a graph filter generated by the classical graph Laplacian matrix.The results show our method can achieve a comparable denoising performance with several state-of-the-art denoising methods.

TV/L2-based image denoisingalgorithm with automaticparameter selection

In order to improve the adaptiveness of TV/L2-based image denoising algorithm in differ- ent signal-to-noise ratio （SNR） environments, an iterative denoising method with automatic parame- ter selection is proposed. Based upon the close connection between optimization function of denois- ing problem and regularization parameter, an updating model is built to select the regularized param- eter. Both the parameter and the objective function are dynamically updated in alternating minimiza- tion iterations, consequently, it can make the algorithm work in different SNR environments. Mean- while, a strategy for choosing the initial regularization parameter is presented. Considering Morozov discrepancy principle, a convex function with respect to the regularization parameter is modeled. Via the optimization method, it is easy and fast to find the convergence value of parameter, which is suitable for the iterative image denoising algorithm. Comparing with several state-of-the-art algo- rithms, many experiments confirm that the denoising algorithm with the proposed parameter selec- tion is highly effective to evaluate peak signal-to-noise ratio （PSNR） and structural similarity

R-L Method and BLS-GSM Denoising for Penumbra Image Reconstruction

When neutron yield is very low, reconstruction of coding penumbra image is rather difficult. In this paper, low-yield （109） 14 MeV neutron penumbra imaging was simulated by Monte Carlo method. The Richardson Lucy （R-L） iteration method was proposed to incorporated with Bayesian least square-Gaussian scale mixture model （BLS-GSM） wavelet denoising for the simulated image. Optimal number of R-L iterations was gotten by a large number of tests. The results show that compared with Wiener method and median filter denoising, this method is better in restraining background noise, the correlation coefficient Rsr between the reconstructed and the real images is larger, and the reconstruction result is better.

An Optimal Weight Method for CT Image Denoising

This paper proposes a novel exemplar- based method for reducing noise in computed tomography （CT） images. In the proposed method, denoising is performed on each block with the help of a given database of standard image blocks. For each noisy block, its denoised version is the best sparse positive linear combination of the blocks in the database. We formulate the problem as a constrained optimization problem such that the solution is the denoised block. Experimental results demonstrate the good performance of the proposed method over current state-of-the-art denoising methods, in terms of both objective and subjective evaluations.

Low-Dose CT Image Denoising Based on Improved WGAN-gp

In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.

Denoising of Chinese calligraphy tablet images based on run-length statistics and structure characteristic of character strokes

In this paper, a novel approach is proposed for denoising of Chinese calligraphy tablet documents. The method includes two phases: First, a partial differential equations (PDE) based the total variation model and Otsu thresholding method are used to preprocess the calligraphy document image. Second, a new method based on run-length statistics and structure charac- teristics of Chinese characters is proposed to remove some random and ant-like noises. This includes the optimal threshold se- lection from histogram of run-length probability density, and improved Hough transform algorithm for line shape noise detection and removal. Examples are given in the paper to demonstrate the proposed method.

A New Image Denoising Scheme Using Soft-Thresholding

The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. The experimental results show that our proposed scheme has better performance than the VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e., visual quality of the image.

Image Denoising Using Local Adaptive Least Squares Support Vector Regression

而非试图在空间领域把信号与噪音分开，在一个变换领域工作经常是有益的。以前的工作上的大楼，基于本地人，适应最少的广场支持向量回归的一个新奇降噪方法被建议。Gaussian 噪音污染的实像上的调查证明了建议方法能完成一项可接受的贸易离开在噪音移动之间并且边和细节变光滑。

Image Denoising Combining the P-M Model and the LLT Model

In this paper, we present a noise removal technique by combining the P-M model with the LLT model. The combined technique takes full use of the advantage of both filters which is able to preserve edges and simultaneously overcomes the staircase effect. We use a weighting function in our model, and compare this model with the P-M model as well as other fourth-order functional both in theory and numerical experiment.

Improving Mutual Coherence with Non-Uniform Discretization of Orthogonal Function for Image Denoising Application

This paper presented a novel method on designing redundant dictionary from known orthogonal functions. Usual way of discretization of continuous functions is uniform sampling. Our experiments show that dividing the function definition interval with non-uniform measure makes the redundant dictionary sparser and it is suitable for image denoising via sparse and redundant dictionary. In this case the problem is to find an appropriate measure in order to make each atom of dictionary. It has shown that in sparse approximation context, incoherent dictionary is suitable for sparse approximation method. According to this fact we define some optimization problems to find the best parameter of distribution measure (in our study normal distribution). For better convergence to optimum point we used Genetic Algorithm (GA) with enough diversity on initial population. We show the effect of this type of dictionary design on exact sparse recovery support. Our results also show the advantage of this design method on image denoising task.

NEW SAR IMAGE INTERPRETATION METHOD OF AIRCRAFT BASED ON JOINT TIME-FREQUENCY ANALYSIS

With the continuous improvement of Synthetic Aperture Radar(SAR) resolution, interpreting the small targets like aircraft in SAR images becomes possible and turn out to be a hot spot in SAR application research. However, due to the complexity of SAR imaging mechanism, interpreting targets in SAR images is a tough problem. This paper presents a new aircraft interpretation method based on the joint time-frequency analysis and multi-dimensional contrasting of basic structures. Moreover, SAR data acquisition experiment is designed for interpreting the aircraft. Analyzing the experiment data with our method, the result shows that the proposed method largely makes use of the SAR data information. The reasonable results can provide some auxiliary support for the SAR images manual interpretation.