Removal of Random Valued Impulsive Noise

In digital Image Processing, removal of noise is a highly demanded area of research.Impulsive noise is common in images which arise at the time of image acquisition and or transmission of images. Impulsive noise can be classified into two categories,namely Salt & Pepper Noise (SPN) and Random Valued Impulsive Noise (RVIN). Removal SPN is easier as compared to RVIN due to its characteristics. The present work concentrates on removal of RVIN from images.Most of the nonlinear filters used in removal of impulsive noise work in two phases,i.e. detection followed by filtering only the corrupted pixels keeping uncorrupted ones intact. Performance of such filters is dependent on the performance of detection schemes. In this work, thrust has been put to devise an accurate detection scheme and a novel weighted median filtering mechanism. The proposed detection scheme utilizes double difference among the pixels in a test window. The difference is computed along four directions namely, horizontal, vertical,and two diagonals to capture the edge direction if any exists. This helps to identify, whether the test pixels under consideration is an edge pixel or a noisy one. Subsequently, the corrupted pixels are passed through in weighted median filter, where more weights are assigned to those pixels which lie in a minimum variance direction among all the four. Extensive simulation has been carried out at various noise conditions and with different standard images. Comparative analysis has been made with existing standard schemes with suitable parameters such as Peak Signal to Noise Ratio (PSNR), fault detection and misses. It has been observed in general that the proposed schemes outperforms its counterparts at low and medium noise conditions and performs at par at high noise conditions with low computational overhead. The low computational requirements have been substantiated with number of operations required for single window operation and overall time required for detection and filtering operation. In addition, every detector utilizes a threshold value which is compared with a predefined computed value to decide whether the pixel under consideration is corrupted. Fixed threshold may perform well for one image at a particular noise condition. However, generalization is not possible for a fixed threshold. Hence, requirement for an adaptive threshold is realized. In the later part of this thesis, we propose an impulsive detection scheme using an adaptive threshold. The adaptive threshold is determined from an Artificial Neural Network (ANN) using various statistical parameters of noisy image like (µ, σ2, µ4) as inputs. The performance of this scheme is also compared with simulation results

Impulse noise removal by median-type noise detectors and edge-preserving regularization.

Ho Chung Wa.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references.Abstracts in English and Chinese.Introduction --- p.6Paper I --- p.13Paper II --- p.34Concluding Remark --- p.5

Image enhancement techniques applied to solar feature detection

This dissertation presents the development of automatic image enhancement techniques for solar feature detection. The new method allows for detection and tracking of the evolution of filaments in solar images. Series of H-alpha full-disk images are taken in regular time intervals to observe the changes of the solar disk features. In each picture, the solar chromosphere filaments are identified for further evolution examination. The initial preprocessing step involves local thresholding to convert grayscale images into black-and-white pictures with chromosphere granularity enhanced. An alternative preprocessing method, based on image normalization and global thresholding is presented. The next step employs morphological closing operations with multi-directional linear structuring elements to extract elongated shapes in the image. After logical union of directional filtering results, the remaining noise is removed from the final outcome using morphological dilation and erosion with a circular structuring element. Experimental results show that the developed techniques can achieve excellent results in detecting large filaments and good detection rates for small filaments. The final chapter discusses proposed directions of the future research and applications to other areas of solar image processing, in particular to detection of solar flares, plages and sunspots

Intelligent Assisting Tools for Endodontic Treatment

The integration of image processing in novel systems bids fair to significantly improve the endodontic practice in the near future. Also, the attempt to automatically locate and classify the root canals may result in significantly decreased chair time for both the patient and the practitioner. We focus on the shapes of human root canals and their automatic classification, methods for automatic processing, and center line identification of tooth root canal as defined previously. We introduce some micro-computed tomography image analysis methods possible for clinical implementation of cone beam computed tomography image analysis in endodontics and limitations of novel techniques. In this chapter, we present our results of segmentation and root canal identification of cone beam computed tomography images

Robust detail-preserving signal extraction

We discuss robust filtering procedures for signal extraction from noisy time series. Particular attention is paid to the preservation of relevant signal details like abrupt shifts. moving averages and running medians are widely used but have shortcomings when large spikes (outliers) or trends occur. Modifications like modified trimmed means and linear median hybrid filters combine advantages of both approaches, but they do not completely overcome the difficulties. Better solutions can be based on robust regression techniques, which even work in real time because of increased computational power and faster algorithms. Reviewing previous work we present filters for robust signal extraction and discuss their merits for preserving trends, abrupt shifts and local extremes as well as for the removal of outliers. --

Fully-Automatic Multiresolution Idealization for Filtered Ion Channel Recordings: Flickering Event Detection

We propose a new model-free segmentation method, JULES, which combines recent statistical multiresolution techniques with local deconvolution for idealization of ion channel recordings. The multiresolution criterion takes into account scales down to the sampling rate enabling the detection of flickering events, i.e., events on small temporal scales, even below the filter frequency. For such small scales the deconvolution step allows for a precise determination of dwell times and, in particular, of amplitude levels, a task which is not possible with common thresholding methods. This is confirmed theoretically and in a comprehensive simulation study. In addition, JULES can be applied as a preprocessing method for a refined hidden Markov analysis. Our new methodolodgy allows us to show that gramicidin A flickering events have the same amplitude as the slow gating events. JULES is available as an R function jules in the package clampSeg

SALT AND PEPPER NOISE REMOVING IN GRAY IMAGES BASED ON FUZZY LOGIC

Digital image processing plays a pivotal role in various fields, from medical imaging to surveillance systems. However, the acquired images are often susceptible to various types of noise, such in the form of salt and pepper noise, which can severely degrade image quality and hinder subsequent analysis.In this study, we introduce a fuzzy impulse noise removal algorithm as a potential solution. The efficiency of the suggested algorithms is assessed by comparing their performance to various existing noise removal methods. Objective measurements, including peak signal-to-noise ratio and mean square error, are used to evaluate the results. The findings demonstrate that the proposed algorithms deliver excellent outcomes in noise reduction and image detail preservation across a broad range of noise densities

The Future of the Operating Room: Surgical Preplanning and Navigation using High Accuracy Ultra-Wideband Positioning and Advanced Bone Measurement

This dissertation embodies the diversity and creativity of my research, of which much has been peer-reviewed, published in archival quality journals, and presented nationally and internationally. Portions of the work described herein have been published in the fields of image processing, forensic anthropology, physical anthropology, biomedical engineering, clinical orthopedics, and microwave engineering. The problem studied is primarily that of developing the tools and technologies for a next-generation surgical navigation system. The discussion focuses on the underlying technologies of a novel microwave positioning subsystem and a bone analysis subsystem. The methodologies behind each of these technologies are presented in the context of the overall system with the salient results helping to elucidate the difficult facets of the problem. The microwave positioning system is currently the highest accuracy wireless ultra-wideband positioning system that can be found in the literature. The challenges in producing a system with these capabilities are many, and the research and development in solving these problems should further the art of high accuracy pulse-based positioning

A usability study of physiological measurement in school using wearable sensors

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps

New Sequential Partial Update Normalized Least Mean M-estimate Algorithms for Stereophonic Acoustic Echo Cancellation

ABSTRACT This paper proposes a family of new robust adaptive filtering algorithms for stereophonic acoustic echo cancellation in impulsive noise environment. The new algorithms employ sequential partial update scheme to reduce computational complexity, which is desirable in long echo path case. On the other hand, by employing robust M-estimate technique, the new algorithms become more robust to impulsive noises compared to their conventional least squarebased counterparts. These two advantages enable the proposed algorithms to be good alternatives for stereophonic echo cancellation. Experiments are also conducted to verify their efficiency