In this paper we introduce a new localization framework for wavelet transforms, such as the 1D wavelet transform and the Shearlet transform. Our goal is to design nonadaptive window functions that promote sparsity in some sense. For that, we introduce a framework for analyzing localization aspects of window functions. Our localization theory diverges from the conventional theory in two ways. Fi...

Although the conventional image processing methods can detect fabric defects, defect detection is an open research problem due to diversity of types. In this paper, feasibility VGG16 deep learning architecture for has been demonstrated. A new database used. The pre-trained model on built. Thus, training time reduced. experimental results show that outperforms traditional Shearlet transform and ...

Abstract One of the primary purposes seismic stratigraphy is to evaluate components layer relationships within a depositional chronology. Prestack images contain wealth information, such as variations in offset and azimuth event, naturally produce higher-resolution facies analysis results than poststack data. However, prestack data usually suffer from potential unreliability issues due low sign...

Wavelets and their associated transforms are highly efficient when approximating and analyzing onedimensional signals. However, multivariate signals such as images or videos typically exhibit curvilinear singularities, which wavelets are provably deficient of sparsely approximating and also of analyzing in the sense of, for instance, detecting their direction. Shearlets are a directional repres...

Traditional noise removal methods like Non-Local Means create spurious boundaries inside regular zones. Visushrink removes too many coefficients and yields recovered images that are overly smoothed. In Bayesshrink method, sharp features are preserved. However, PSNR (Peak Signal-to-Noise Ratio) is considerably low. BLS-GSM generates some discontinuous information during the course of denoising a...

Correspondence: [email protected] UMIACS, University of Maryland, College Park, MD 20742-3275, USA Full list of author information is available at the end of the article †Equal contributor Abstract Shearlets have emerged in recent years as one of of the most successful methods for the multiscale analysis of multidimensional signals. Unlike wavelets, shearlets form a pyramid of well-localize...