High Density Noise Removal by Cascading Algorithms

An advanced non-linear cascading filter algorithm for the removal of high density salt and pepper noise from the digital images is proposed. The proposed method consists of two stages. The first stage Decision base Median Filter (DMF) acts as the preliminary noise removal algorithm. The second stage is either Modified Decision Base Partial Trimmed Global Mean Filter (MDBPTGMF) or Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) which is used to remove the remaining noise and enhance the image quality. The DMF algorithm performs well at low noise density but it fails to remove the noise at medium and high level. The MDBPTGMF and MDUTMF have excellent performance at low, medium and high noise density but these reduce the image quality and blur the image at high noise level. So the basic idea behind this paper is to combine the advantages of the filters used in both the stages to remove the Salt and Pepper noise and enhance the image quality at all the noise density level. The proposed method is tested against different gray scale images and it gives better Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF) than the Adaptive Median Filter (AMF), Decision Base Unsymmetric Trimmed Median Filter (DBUTMF), Modified Decision Base Unsymmetric Trimmed Median Filter (MDBUTMF) and Decision Base Partial Trimmed Global Mean Filter (DBPTGMF).Comment: 6 pages, 6 figure

A Survey of Non-Linear Filtering Techniques For Image Noise Removal

Image is captured or noninheritable by any image capturing device like camera or scanner and then it is stored in the mass storage of the computer system. In many of these applications the existence of impulsive noise among the noninheritable pictures is one altogether common problems. This noise is characterized by spots on the image and is usually related to the innate image because of errors in image sensors and information transmission. Now-a-days there are numerous strategies that are offered to remove noise from digital images. Most of the novel methodology includes 2 stages: the primary stage is to find the noise within the image and the second stage is to eliminate the noise from the image. This paper explores the varied novel methods for the removal of noise from the digital images. The distinctive feature of the all the described filters is that offers well line, edge and detail preservation performance while, at the constant time, effectively removing noise from the input image. In later section, we present a short introduction for various strategies for noise reduction in digital images

AN FPGA IMPLEMENTATION OF MODIFIED DECISION BASED UNSYMMETRICAL TRIMMED MEDIAN FILTER FOR THE REMOVAL OF SALT AND PEPPER NOISE IN DIGITAL IMAGES

A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. Images are often corrupted by impulse noise during acquisition and transmission; thus, an efficient noise suppression technique is required before subsequent image processing operations. Median filter (MF) is widely used in noise removal methods due to its denoising capability and computational efficiency. However, it is effective only for low noise densities. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than denoising techniques. Especially, the proposed method can preserve edges very well while removing salt and pepper noise. Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF) shows better results at low and medium noise densities. At high noise densities, their performance is poor. A new algorithm to remove high-density salt and pepper noise using modified Decision Based Unsymmetric Trimmed Median Filter (DBUTMF) is proposed. The proposed algorithm replaces the noisy pixel by trimmed median. Since our algorithm is algorithmically simple, it is very suitable to be applied to many real-time applications and higher noise densities. 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. 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)

Hybrid filtering technique to remove noise of high density from digital images

Noise removal is one of the greatest challenges among the researchers, noise removal algorithms vary with the application areas and the type of images and noises. The work proposes a novel hybrid filter which is capable of predicting the best filter for every pixel using neural network and choose the best technique to remove noise with 3x3 mask operation. Proposed algorithm first train the neural network for various filters like mean, median, mode, geometrical mean, arithmetic mean and will use to remove noise later on. Later, the proposed method is compared with the existing techniques using the parameters MAE, PSNR, MSE and IEF. The experimental result shows that proposed method gives better performance in comparison with MF, AMF and other existing noise removal algorithms and improves the values of various parameter

Effective Approach for Extracting Noise from Digital Image and Real Time Data using Filtering Technique

Digital image are made out of pixels and we know pixel is the smallest component of a picture. Every pixel speaks to the dark level for highly contrasting photographs at a solitary point in the image, so a pixel can be spoken to by a small speck of particular shading. In an image pixel having intensity in range of 0-255 and if pixel having intensity zero it means black and having intensity 255 it means white and in between them then considered as gray level. There are various types of images and various types of noises occurred and to remove them diverse filters are available and every filter are having own advantage and disadvantages and suitable for a particular types of noised which it can remove efficiently. In our research work our main target is to fetch out salt and pepper noise. In base paper at two levels of S&P noise filter is used to denoise the image to get various parameters. But in our research work salt and pepper noise at various levels targeted and removed efficiently with parameters PSNR, MSE and IEF. Noise is random in nature and it can be mixed with image anywhere therefore diverse noise models were studied deeply. Restoration efficiency was checked by PSNR and mean square error (MSE) into considerations

An overview of multi-filters for eliminating impulse noise for digital images

An image through the digitization process is referred to as a digital image. The quality of the digital image may be degenerating due to interferences on the acquisition, transmission, extraction, etc. This attracted the attention of many researchers to study the causes of damage to the information in the image. In addition to finding cause of image damage, the researchers also looking for ways to overcome this problem. There are many filtering techniques that have been introduced to deal the damage to the information in the image. In addition to eliminating noise from the image, filtering techniques also aims to maintain the originality of the features in the image. Among the many research papers on image filtering there is a lack of review papers which are an important to facilitate researchers in understanding the differences in each filtering technique. Additionally, it helps researchers determine the direction of research conducted based on the results of previous research. Therefore, this paper presents a review of several filtering techniques that have been developed so far

Noise Suppression in Images by Median Filter

A new and efficient algorithm for high-density salt and pepper noise removal in images and videos is proposed. In the transmission of images over channels, images are corrupted by salt and pepper noise, due to faulty communications. Salt and Pepper noise is also referred to as Impulse noise. The objective of filtering is to remove the impulses so that the noise free image is fully recovered with minimum signal distortion. Noise removal can be achieved, by using a number of existing linear filtering techniques. We will deal with the images corrupted by salt-and-pepper noise in which the noisy pixels can take only the maximum or minimum values (i.e. 0 or 255 for 8-bit grayscale images)