Determining displacement and strain maps immune from aliasing effect with the grid method

Spatial aliasing may affect methods based on grid image processing to retrieve displacement and strain maps in experimental mechanics. Such methods aim at estimating these maps on the surface of a specimen subjected to a loading test. Aliasing, which is often not noticeable to the naked eye in the grid images, may give spurious fringes in the strain maps. This paper presents an analysis of aliasing in this context and provides the reader with simple guidelines to minimize the effect of aliasing on strain maps extracted from grid images.     

A numerical study of noise reduction in ducts with flow by anti-sound

I.IntroductionAnti-soundisalsocal1edactivenoisecontro1(ANC).Itsbasicidea,presentedintheLueg'spatentinl936l'l,isthatthenoisereductionisobtainedbyuseofthesignalpro-cessingofthepreliminarysoundsource(i.e.noisesource)toformcoherenceinfluencebe-twccnthepreliminarysoundsourceandthesecondarysoundsourcc(i.c.anti-soundsource).Therearesomeadvantagesofanti-soundsuchasactivecontro1,lcsseffectonthecharacteristicsofnoisesourccandmorereductionoflowfrequcncynoisc.Inrecentyears,therewerealotoftheoreticalande…     

An effective noise removal technique for recovering the shapes of diamond abrasive grains in SLM images degraded by clustered spike noise

A new noise-removal technique is applied to scanning laser microscopic (SLM) images to remove clustered spike noise in the images and to recover the shapes of diamond abrasive grains degraded by the noise. For achievement of this purpose, noise points in the SLM image are accurately detected by taking advantage of their properties in the space and spatial-frequency regions. The noise points are removed by a method of smoothing that is based on linear interpolation; that is, their pixel values are replaced by the interpolated values of their non-noise neighboring points. Noise-point information in the space region is acquired from image segmentation based on pixel classification, while noise-point information in the frequency region is derived from redundant wavelet decomposition for the SLM image. Fisher's linear discriminant method is used to yield the two noise-point images. The degraded grain shapes in the SLM images at different noise levels are satisfactorily recovered with a single iteration of smoothing without losses in sharp edges although a single smoothing needed four interpolations. Thus, the present noise-removal technique is shown to be effective for recovering the original shapes of the grains in every SLM image.