Distance Measures for Reduced Ordering Based Vector Filters

Reduced ordering based vector filters have proved successful in removing long-tailed noise from color images while preserving edges and fine image details. These filters commonly utilize variants of the Minkowski distance to order the color vectors with the aim of distinguishing between noisy and noise-free vectors. In this paper, we review various alternative distance measures and evaluate their performance on a large and diverse set of images using several effectiveness and efficiency criteria. The results demonstrate that there are in fact strong alternatives to the popular Minkowski metrics

A superior edge preserving filter with a systematic analysis

A new, adaptive, edge preserving filter for use in image processing is presented. It had superior performance when compared to other filters. Termed the contiguous K-average, it aggregates pixels by examining all pixels contiguous to an existing cluster and adding the pixel closest to the mean of the existing cluster. The process is iterated until K pixels were accumulated. Rather than simply compare the visual results of processing with this operator to other filters, some approaches were developed which allow quantitative evaluation of how well and filter performs. Particular attention is given to the standard deviation of noise within a feature and the stability of imagery under iterative processing. Demonstrations illustrate the performance of several filters to discriminate against noise and retain edges, the effect of filtering as a preprocessing step, and the utility of the contiguous K-average filter when used with remote sensing data

Photometry and Photometric Redshifts of Faint Galaxies in the Hubble Deep Field South NICMOS Field

We present a catalog of photometry and photometric redshifts of 335 faint objects in the HDF-S NICMOS field. The analysis is based on (1) infrared images obtained with the Hubble Space Telescope (HST) using the Near Infrared Camera and Multi-Object Spectrograph (NICMOS) with the F110W, F160W, and F222M filters, (2) an optical image obtained with HST using the Space Telescope Imaging Spectrograph (STIS) with no filter, and (3) optical images obtained with the European Southern Observatory (ESO) Very Large Telescope (VLT) with U, B, V, R, and I filters. The primary utility of the catalog of photometric redshifts is as a survey of faint galaxies detected in the NICMOS F160W and F222M images. The sensitivity of the survey varies significantly with position, reaching a limiting depth of AB(16,000) ~ 28.7 and covering 1.01 arcmin^2 to AB(16,000) = 27 and 1.05 arcmin^2 to AB(16,000) = 26.5. The catalog of photometric redshifts identifies 21 galaxies (or 6% of the total) of redshift z > 5, 8 galaxies (or 2% of the total) of redshift z > 10, and 11 galaxies (or 3% of the total) of best-fit spectral type E/S0, of which 5 galaxies (or 1% of the total) are of redshift z > 1.Comment: 33 pages, 10 figures, accepted for publication in the Astrophysical Journal, August 1, 2000 issu

Redundancy of stereoscopic images: Experimental Evaluation

With the recent advancement in visualization devices over the last years, we are seeing a growing market for stereoscopic content. In order to convey 3D content by means of stereoscopic displays, one needs to transmit and display at least 2 points of view of the video content. This has profound implications on the resources required to transmit the content, as well as demands on the complexity of the visualization system. It is known that stereoscopic images are redundant, which may prove useful for compression and may have positive effect on the construction of the visualization device. In this paper we describe an experimental evaluation of data redundancy in color stereoscopic images. In the experiments with computer generated and real life and test stereo images, several observers visually tested the stereopsis threshold and accuracy of parallax measuring in anaglyphs and stereograms as functions of the blur degree of one of two stereo images and color saturation threshold in one of two stereo images for which full color 3D perception with no visible color degradations is maintained. The experiments support a theoretical estimate that one has to add, to data required to reproduce one of two stereoscopic images, only several percents of that amount of data in order to achieve stereoscopic perception

Systematic approach to nonlinear filtering associated with aggregation operators. Part 1. SISO-filters

There are various methods to help restore an image from noisy distortions. Each technique has its advantages and disadvantages. Selecting the appropriate method plays a major role in getting the desired image. Noise removal or noise reduction can be done on an image by linear or nonlinear filtering. The more popular linear technique is based on average (on mean) linear operators. Denoising via linear filters normally does not perform satisfactorily since both noise and edges contain high frequencies. Therefore, any practical denoising model has to be nonlinear. In this work, we introduce and analyze a new class of nonlinear SISO-filters that have their roots in aggregation operator theory. We show that a large body of non-linear filters proposed to date constitute a proper subset of aggregation filters. (C) 2017 The Authors. Published by Elsevier Ltd.This work was supported by grants the RFBR No. 17-07-00886 and by Ural State Forest Engineering's Center of Excellence in "Quantum and Classical Information Technologies for Remote Sensing Systems"

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare dierent edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS

AN OPTIMAL NOISE REMOVAL APPROACH FOR LATERAL SKULL IMAGES

We are using medical imagingdevices to scan the organs of human to identify the different diseases. Thediseases are diagnosed by medical images which are produced by various medicaldevices like Ultra Sound, Magnetic Resonance Image, Computed Tomography (CT),Ultra sound and X ray Medical devices. Images are prone to different types ofnoises due  medical devices. Poison noiseis commonly found in X ray images. The filters namely, Unsharp, Mean, Median,Gaussian and Weiner are used for comparative study analysis. The computedRadiography CR image qualities are improved by our comparative study offilters. The aim of this paper is to identify the best poison noise removal filterfrom the comparative study analysis of five filters. The best filter isestimated by calculating Peak Signal Noise Ratio(PSNR), Root Mean Square Error(RMSE) and Means Square Error(MSE)