Mixed Noise Removal with External Parameter in Image Denoising (original) (raw)

Image Denoising Using Hybrid Filtering Techniques

IJCRT, 2021

Image is a key of digital data which is used in many studies and research work as dataset. These datasets are compromised due to distortion which is caused by the presence of noise. Occurrence of noise is found while capturing image, transmission of pictorial data over different networks, etc. An image can be corrupted due to various factors, among them Noise plays a vital role in image corruption and this noise can also exist individually with varying intensities of different noise factor or also as hybrid noise [i.e Combination of different noises], hence removal of noise becomes a main challenge in image processing. In general, the results of denoising affect the quality of the image processing approaches. The nature of the noise removal issue depends on the sort of the noise corrupting the image. The Salt and Pepper, Gaussian, Poisson and Speckle noise are the noises that usually affect the images. To restore these degraded images, many de-noising algorithm has been evolved and one among them are filtering techniques. In this research work, three filters are considered for denoising i.e. Weiner filter, Gaussian filter and Median filter. The current work is implemented on gray scale images and the evaluation of these algorithms is done by the measure of the PSNR and MSE values. In addition, we propose to use hybrid filter for denoising images that can be corrupted by individual or hybrid noise.

A Comparative Study of Mixed Noise Removal Techniques

International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014

Mixed noises are a characteristic of combined noises acting on a single carrier. Various mechanisms in recent past have been given in literature to restore images corrupted with Poisson and impulse mixed noise. This paper compares mixed noise removal techniques such as: Peer Group averaging (PGA), Vector Median Filter (VMF), Vector Direction Filter (VDF), Fuzzy Peer Group Averaging (FPGA), and Fuzzy Vector Median Filter (FVMF) on the basis of performance metrics such as Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE), Mean Square Error (MSE) and time complexity. The image size and the noise density is varied so as record these performance metrics. All the above mentioned techniques were implemented in MATLAB-11. The simulation and result shows that FVMF introduces blurring of edged but provide an output of highest PSNR value, especially for large sized images. However, for smaller images PGA provides best results of PSNR and hence a good quality of de-noised image. Also it is observed that with increase in image size the quality of the resulting image improves as the value of PSNR also increases but on increasing the impulse noise density with constant image size the image quality decreases with a constant decrease in the PSNR value.

A Review of Image Denoisng Techniques

International Journal of Engineering Sciences & Research Technology, 2014

One of the most fundamental challenges in the field of image processing is image denoising, where the primary objective is to estimate the original image by removing noise from a noisy version of the image. Many algorithms have been proposed so far for removal of noise from the digital images. This paper review different image denoising techniques. It has been found that the most of the previous denoising techniques like gaussian filtering; bilateral filtering may remove fine details from the image. So a non local method known as non-local means solve this problem. This technique estimates a noise-free pixel as a weighted average of all similar pixels in the image. Non local euclidean median is a image denoising technique. Denoising performance of a noisy image improved by replacing the mean by the euclidean median and this new denoising algorithm the non-local euclidean medians (NLEM). This technique proves that the median is more vigorous to outliers than the mean

A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications

Cognitive Internet of Medical Things for Smart Healthcare, 2020

Noise reduction is a perplexing undertaking for the researchers in digital image processing and has a wide range of applications in automation, IoT (Internet of Things), medicine, etc. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as a gathering of data and the existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Digital images are debased through noise through its transmission and procurement. Noisy image reduces the image contrast, edges, textures, object details, and resolution, thereby decreasing the performance of postprocessing algorithms. This paper mainly focuses on Gaussian noise, salt and pepper noise, uniform noise, speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as a reorganization of images. Here four types of noises have been undertaken and applied to process images. Besides linear and nonlinear filtering methods like Gaussian filter, median filter, mean filter and Weiner filter applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), average difference value (AD) and maximum difference value (MD) to diminish the noises without corrupting the medical image data.

COMPARISON OF DENOISING TECHNIQUES IN MONOCHROME AND COLOUR IMAGES

Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This paper reviews the denoising algorithms, using filtering approach, and performs their comparative study. The noise model which we have used that is Gaussian noise, salt and pepper noise,. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. The filtering approach has been proved to be the best when the image is corrupted with salt and pepper noise.. A quantitative measure of comparison is provided by the signal to noise ratio of the image as well as mean absolute error in the image Key words:-impulse noise, high density noise, median filter, non linear filter, Adaptive centre weighted median filter.

Comparative Study on Noise Removal Techniques in Digital Images

Journal of emerging technologies and innovative research, 2019

A visual image is rich in information. Digital images plays very significant role in our day today life. The influence and impact of digital images on modern society, science, technology and art are tremendous. Image Processing is a popular field from Medical to Robotics and the list pretty endless. Digital images are corrupted with various types of noises during acquisition and transmission. The main challenge in digital image processing is to remove noise from the original image. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. The scope of the paper is to focus on different types of noises and denoising techniques which are encountered in digital images.

A Review : Various Image Denoising Techniques

International Journal of Computer Applications, 2014

Removal of noise is an essential and challengeable operation in image processing. Before performing any process, images must be first restored. Images may be corrupted by noise during image transmission through electronic media. Noise effect always corrupts any recorded image which is much more harmful for future process. To overcome the problem of noise level in digital images this paper present a review of different image denoising method. In this paper various filters are used for image denoising. This proposed method adopt first and second order mean filter (FSOMF) in which for first phase we detect the impulse noise. And the second phase which is also called as filtering phase replaces the detected noise pixel. Finally able to show in our experimental result of proposed method FSOMF, is capable of filtering of impulse noise.

An Efficient Noise Removal Algorithm Based on the Noise Density

Gray scale and color images are affected by salt and pepper (impulse noise) which is encountered frequently in acquisition, transmission and processing of images. Proposed method achieves restoration of noisy image by usage of highly efficient filters which adapt based on the existing noise density in the image. The proposed algorithm involves two stages: noise density calculation of the corrupted image followed by noise detection and filtering. As noise density increases, the window size is increased which gives better results. The proposed algorithm replaces the pixels with values 0 and 255 with the median of the window considered if the window also includes pixel values other than 0 or 255. If the window considered contains pixels with values 0 and 255 only then they are replaced by the mean value of all elements present in the selected window. Since the proposed algorithm chooses the filter based on noise density, it works better than Median filter, Progressive Switched Median Filter (PSMF), Untrimmed Median Filter (UMF) and Adaptive Median Filter (AMF) considered individually. The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

Image Denoising Techniques-An Overview

A fundamental step in image processing is the step of removing various kinds of noise from the image. Sources of noise in an image mostly occur during storage, transmission and acquisition of the image .Image denoising is a applicable issue found in diverse image processing and computer vision problems. There are various existing methods to denoise image. The important property of a good image denoising model is that it should completely remove noise as far as possible as well as preserve edges. The image denosing technique will be mainly depending on the type of the image and noise in cooperating with it. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. This paper presents a review of some noise models and significant work in the area of image denoising.

A Mixed Noise Removal Method Based on Total Variation

Today because of technology limits, digital images always include some defects, such as noise. Noise reduces image quality and affects the result of image processing. In almost cases, noise is Gaussian one. While in biomedical images, the usual noise is a combination of Poisson and Gaussian noises. This combination is naturally considered as a superposition of Gaussian noise over Poisson noise. In this paper, we propose a method to remove such a type of mixed noise based on a new approach: we consider the superposition of noises like a linear combination. We use the idea of the total variation of an image intensity (brightness) function to remove this combination of noises. Povzetek: Članek predlaga izvirno kombinacijo Gaussovega in Poissonovega filtra za filtriranje šuma v slikah.