Image Denoising Using Multiwavelet Transform with Different Filters and Rules

<p class="0abstract">Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorithm is applied Salt&amp; pepper noise with different levels for grayscale test images. The quality of the denoised image is evaluated by using Peak Signal to Noise Ratio (PSNR). Depend on the value of PSNR that explained in the result section; we conclude that the (Tri-State Median filter) is better than (Switching Median filter) in denoising image quality, according to the results of applying rules the result of the Shrinking rule for each filter shows that the best result using first the Bivariate Shrink.</p>

Switching median filter for suppressing multi-pixel impulse noise

This paper proposes a new switching median filter for suppressing multi-pixel impulse noise in X-ray images. A multi-pixel impulse is understood as a set of several neighboring pixels, the intensity of each significantly exceeds background intensity. Multi-pixel noise can occur, for example, due to the blooming effect, the reason being the limited value of pixel saturation capacity. This article defines the thresholds for the intensity increment relative to the eight immediate neighbors, above which the current pixel is processed by the median filter. The dependence of these thresholds on the number of pixels in an impulse is presented. The proposed algorithm is based on the median filtering process, which consists of several iterations. In this case, the filter has the smallest possible size, which minimizes image distortion during processing. In particular, to exclude a single-pixel impulse, pixel processing is turned on when intensity surge exceeds 3.5 with the grayscale value ranging from 0 to 1. At the same time, to exclude nine-pixel impulses, three iterations are required with the following thresholds: the first iteration with a threshold 2.0; the second iteration also with a threshold 2.0 and the third iteration with a threshold 3.5. The algorithm proposed was tested on real X-ray images corrupted by multi-pixel impulse noise. The algorithm is not only simple, but also reliable and suitable for real-time implementation and application. The efficiency of the technique is shown in comparison with other known filtering methods with respect to the degree of noise suppression. The main result of the testing is that only the proposed method allows excluding multi-pixel noise. Other advantage of the algorithm is its weak effect on the level of Gaussian noise leading to the absence of image blurring (or preserving image details) during processing.

Modified Filter Equation with Improved Fuzzy Logic System Based Directional Median Filter for Mixed Noise

DWM (Directional weighted median) filter is very popular in filtering digital image and remove mixed noise. Fuzzy logic is implemented with median filters to improve its performance. In the previous work, fuzzy logic system is implemented with switching median filter and gives better performance than directional median filter as well as switching median filter. Experimenting directional median filter with same fuzzy logic system didn’t yield to better results therefore fuzzy logic parameters has been changes as per strong points of directional weighted median filter and a constant has been included in the filtering equation to improve the results. So in this proposed work, we have successfully implemented directional weighted median filter with fuzzy logic system which is proving better results than DWM and FSMF (Fuzzy Switching Median Filter). PSNR (Peak Signal to Noise Ratio)is used for qualitative analysis of results.

Design and Implementation of Shape and Texture Based Image Segmentation on Morphological Gradient Approach

Identifying and separating objects within an image is a significant challenge due to high object and background variability. This can be obtained through feature extraction approach. There are different ways of extracting the image features. It is based on texture, shape and colour. The present paper aims to study and analyse the various approaches for feature extraction and object recognition. This study proposed a hybrid approach, which is a combination of enhanced Fractal Texture Analysis with Layout Descriptor to overcome the obstacles in image segmentation. It is used to lessen the boundary complexity of the segmented image. First, the image is preprocessed to discard the noise and to retain the adequate details of the image in a perfect way through Adaptive Switching Median Filter. Secondly, it improves the power of the edges detected through a noise-protected edge detector. Finally, it is applied with morphological gradient technique that is a twin function of both shape and texture gradient removal for extorting the qualities of the image. In this way, the proposed methodology directly performs on the colour image which supports to enhance prediction accuracy of the object in terms of colour characteristics that offers better results than the grayscale conversion approach. Moreover, the shape feature is extracted from the preprocessed image depending on the details like compactness, rectangularity, eccentricity and moment invariants.

Performance of Median Filter and its Variants for the Preprocessing of Mammilla Cancer Imagery

This paper presents a comparison of the Median Filter and its variants that are used for the preprocessing of mammilla cancer images in Medical Imaging. Preprocessing of mammilla cancer images is a very important step in their accurate espial. Median filters and its other versions such as Adaptive Median Filter, Progressive Switching Median Filter, and Relaxed Median Filter are applied on a dataset of open source mammilla cancer images for their preprocessing. Their perpetration is compared based on various performance metrics and it’s inferred that the Relaxed Median Filter outperforms the performance of the other Median Filters used.

Automatic Measurement of Garment using Image Processing

In this paper we can employ automatic garment size measuring in textile industry. Textile industry is necessary and wants to be advanced in manufacturing of garments without defects. So that measuring the sizes of garments in industry is still a manual process which affects the precision of measurement. Hence the development of machine vision in image processing technology is used. To improve the automatic measurement a switching median filter is used. It remove the noises in the images. After that boundarylocalization algorithm is used to detect the boundaries of the garment. Then K-means clustering algorithm based edge detection helps to measure the number of pixels occupied in the garment and size of garment is measured from the count of pixels. This garment size measurement is implemented in MATLAB 2014a.

Comparative analysis of Median filter family for Removing High-Density Noise in Magnetic Resonance Images

Magnetic Resonance Imaging (MRI) is a medical indicative test utilized for taking images of the tissue points of interest of the human body. During image acquisition, MRI images can be damaged by many noise signals such as impulse noise. One reason for this noise may be a sharp or sudden disturbance in the image signal. The removal of impulse noise is one of the real difficulties. As of late, numerous image de-noising methods were produced for removing the impulse noise from images. Comparative analysis of known and modern methods of median filter family is presented in this paper. These filters can be categorized as follows: Standard Median Filter; Adaptive Median Filter; Progressive Switching Median Filter; Noise Adaptive Fuzzy Switching Median Filter; and Different Applied Median Filter. The de-noising technique performance for each one is evaluated and compared using Peak Signal Noise Ratio, Structural Similarity index Metric, and Beta metric as quantitative metrics. The experimental results showed that the latest de-noising technique, Different Applied Median Filter (DAMF), produced better results in removing impulse noise compared with the other de-noising techniques. However, this filter produced de-noised image with nonlinear edges in high-density noise. As a result, noise removal from images is one of the low-level images processing which is considered as a first step in many image applications. Therefore, the efficiency of any image processed depends on the efficiency of noise removal technique.

MR Brain Image Segmentation Using k-Means Clustering and Expectation Maximization

Magnetic Resonance Images (MRI) for brain play an important role to identify the disease, dysfunction or disorder of human brain. These images are the primary source to study, analyse and diagnose the anatomy of the brain. This paper presents a new combinatorial technique titled, “MR Brain Image Segmentation using k-Means Clustering and Expectation Maximization (MRB-KMEM)” that performs skull stripping, impulse noise removal, segmentation of brain tissues and classification of brain images. The skull is removed using the technique of morphology-bound brain segmentation and Progressive Switching Median Filter (PSMF) is used to suppress brain image distortion. Further, brain tissues segmentation into white matter and grey matter is performed by KM-EM. The research outcomes can be used to study the features of a brain, its defects and to detect Alzheimer’s disease.