Impulsive noise removal from color images with morphological filtering

This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs a novel approach with morphological filtering for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of morphological filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics and processing speed with that of common successful algorithms.Comment: The 6th international conference on analysis of images, social networks, and texts (AIST 2017), 27-29 July, 2017, Moscow, Russi

Unrestricted multivariate medians for adaptive filtering of color images

Reduction of impulse noise in color images is a fundamental task in the image processing field. A number of approaches have been proposed to solve this problem in literature, and many of them rely on some multivariate median computed on a relevant image window. However, little attention has been paid to the comparative assessment of the distinct medians that can be used for this purpose. In this paper we carry out such a study, and its conclusions lead us to design a new image denoising procedure. Quantitative and qualitative results are shown, which demonstrate the advantages of our method in terms of noise reduction, detail preservation and stability with respect to a selection of well-known proposals.Presentado en el IX Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Hunting Galaxies to (and for) Extinction

In studies of star-forming regions, near-infrared excess (NIRX) sources--objects with intrinsic colors redder than normal stars--constitute both signal (young stars) and noise (e.g. background galaxies). We hunt down (identify) galaxies using near-infrared observations in the Perseus star-forming region by combining structural information, colors, and number density estimates. Galaxies at moderate redshifts (z = 0.1 - 0.5) have colors similar to young stellar objects (YSOs) at both near- and mid-infrared (e.g. Spitzer) wavelengths, which limits our ability to identify YSOs from colors alone. Structural information from high-quality near-infrared observations allows us to better separate YSOs from galaxies, rejecting 2/5 of the YSO candidates identified from Spitzer observations of our regions and potentially extending the YSO luminosity function below K of 15 magnitudes where galaxy contamination dominates. Once they are identified we use galaxies as valuable extra signal for making extinction maps of molecular clouds. Our new iterative procedure: the Galaxies Near Infrared Color Excess method Revisited (GNICER), uses the mean colors of galaxies as a function of magnitude to include them in extinction maps in an unbiased way. GNICER increases the number of background sources used to probe the structure of a cloud, decreasing the noise and increasing the resolution of extinction maps made far from the galactic plane.Comment: 16 pages and 16 figures. Accepted for publication in ApJ. Full resolution version at http://www.cfa.harvard.edu/COMPLETE/papers/Foster_HuntingGalaxies.pd

Classification of Pre-Filtered Multichannel Remote Sensing Images

Open acces: http://www.intechopen.com/books/remote-sensing-advanced-techniques-and-platforms/classification-of-pre-filtered-multichanel-rs-imagesInternational audienc