1,804 research outputs found
Independent Process Analysis without A Priori Dimensional Information
Recently, several algorithms have been proposed for independent subspace
analysis where hidden variables are i.i.d. processes. We show that these
methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our
paper is that we introduce a cascade of algorithms, which aims to solve these
tasks without previous knowledge about the number and the dimensions of the
hidden processes. Our claim is supported by numerical simulations. As a
particular application, we search for subspaces of facial components.Comment: 9 pages, 2 figure
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Mitigating the effects of atmospheric distortion using DT-CWT fusion
This paper describes a new method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which degrades a region of interest (ROI). In order to provide accurate detail from objects behind the dis-torting layer, a simple and efficient frame selection method is proposed to pick informative ROIs from only good-quality frames. We solve the space-variant distortion problem using region-based fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). We also propose an object alignment method for pre-processing the ROI since this can exhibit sig-nificant offsets and distortions between frames. Simple haze removal is used as the final step. The proposed method per-forms very well with atmospherically distorted videos and outperforms other existing methods. Index Terms — Image restoration, fusion, DT-CWT 1
Heterogeneous Multidimensional Data Deblurring
International audienceWe present a new scheme for deconvolution of heterogeneous multidimensional data (\eg spatio-temporal or spatio-spectral). It is derived, in a very general way, following an inverse problem approach. This method exploits the continuity of both object and PSF along the different dimensions to elaborate separable constraints. This improves the effectiveness and the robustness of the deconvolution technique. We demonstrate these improvements by processing real X-ray video sequences and astronomical multi-spectral images
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