9,908 research outputs found
Diversified Texture Synthesis with Feed-forward Networks
Recent progresses on deep discriminative and generative modeling have shown
promising results on texture synthesis. However, existing feed-forward based
methods trade off generality for efficiency, which suffer from many issues,
such as shortage of generality (i.e., build one network per texture), lack of
diversity (i.e., always produce visually identical output) and suboptimality
(i.e., generate less satisfying visual effects). In this work, we focus on
solving these issues for improved texture synthesis. We propose a deep
generative feed-forward network which enables efficient synthesis of multiple
textures within one single network and meaningful interpolation between them.
Meanwhile, a suite of important techniques are introduced to achieve better
convergence and diversity. With extensive experiments, we demonstrate the
effectiveness of the proposed model and techniques for synthesizing a large
number of textures and show its applications with the stylization.Comment: accepted by CVPR201
Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
As a powerful statistical image modeling technique, sparse representation has
been successfully used in various image restoration applications. The success
of sparse representation owes to the development of l1-norm optimization
techniques, and the fact that natural images are intrinsically sparse in some
domain. The image restoration quality largely depends on whether the employed
sparse domain can represent well the underlying image. Considering that the
contents can vary significantly across different images or different patches in
a single image, we propose to learn various sets of bases from a pre-collected
dataset of example image patches, and then for a given patch to be processed,
one set of bases are adaptively selected to characterize the local sparse
domain. We further introduce two adaptive regularization terms into the sparse
representation framework. First, a set of autoregressive (AR) models are
learned from the dataset of example image patches. The best fitted AR models to
a given patch are adaptively selected to regularize the image local structures.
Second, the image non-local self-similarity is introduced as another
regularization term. In addition, the sparsity regularization parameter is
adaptively estimated for better image restoration performance. Extensive
experiments on image deblurring and super-resolution validate that by using
adaptive sparse domain selection and adaptive regularization, the proposed
method achieves much better results than many state-of-the-art algorithms in
terms of both PSNR and visual perception.Comment: 35 pages. This paper is under review in IEEE TI
Single-image super-resolution using sparsity constraints and non-local similarities at multiple resolution scales
Traditional super-resolution methods produce a clean high-resolution image from several observed degraded low-resolution images following an acquisition or degradation model. Such a model describes how each output pixel is related to one or more input pixels and it is called data fidelity term in the regularization framework. Additionally, prior knowledge such as piecewise smoothness can be incorporated to improve the image restoration result. The impact of an observed pixel on the restored pixels is thus local according to the degradation model and the prior knowledge. Therefore, the traditional methods only exploit the spatial redundancy in a local neighborhood and are therefore referred to as local methods. Recently, non-local methods, which make use of similarities between image patches across the whole image, have gained popularity in image restoration in general. In super-resolution literature they are often referred to as exemplar-based methods. In this paper, we exploit the similarity of patches within the same scale (which is related to the class of non-local methods) and across different resolution scales of the same image (which is also related to the fractal-based methods). For patch fusion, we employ a kernel regression algorithm, which yields a blurry and noisy version of the desired high-resolution image. For the final reconstruction step, we develop a novel restoration algorithm. The joint deconvolution/denoising algorithm is based on the split Bregman iterations and, as prior knowledge, the algorithm exploits the sparsity of the image in the shearlet-transformed domain. Initial results indicate an improvement over both classical local and state-of-the art non-local super-resolution methods
Some experimental observations of crack-tip mechanics with displacement data
Estudio de la mecánica en el vértice de la grieta mediante datos de desplazamiento.In the past two decades, crack-tip mechanics has been increasingly studied with full-field
techniques. Within these techniques, Digital Image Correlation (DIC) has been most widely used due to its
many advantages, to extract important crack-tip information, including Stress Intensity Factor (SIF), Crack
Opening Displacement, J-integral, T-stress, closure level, plastic zone size, etc. However, little information is
given in the literature about the experimental setup that provides best estimations for the different parameters.
The current work aims at understanding how the experimental conditions used in DIC influence the crack-tip
information extracted experimentally. The influence of parameters such as magnification factor, size of the
images, position of the images with respect the crack-tip and size of the subset used in the correlation is studied.
The influence is studied in terms of SIF and T-stress by using Williams’ model. The concept of determination of
the K-dominance zone from experimental data has also explored. In this regard, cyclic loading on a fatigue
crack in a compact tension (CT) specimen, made of aluminium 2024-T351 alloy, has been applied and the
surface deformation ahead of the crack tip has been examined. The comparison between theoretical and
experimental values of KI showed that the effect of subset size on the measured KI is negligible compared to
the effect of size of the image.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Introducing SPAIN (SParse Audio INpainter)
A novel sparsity-based algorithm for audio inpainting is proposed. It is an
adaptation of the SPADE algorithm by Kiti\'c et al., originally developed for
audio declipping, to the task of audio inpainting. The new SPAIN (SParse Audio
INpainter) comes in synthesis and analysis variants. Experiments show that both
A-SPAIN and S-SPAIN outperform other sparsity-based inpainting algorithms.
Moreover, A-SPAIN performs on a par with the state-of-the-art method based on
linear prediction in terms of the SNR, and, for larger gaps, SPAIN is even
slightly better in terms of the PEMO-Q psychoacoustic criterion
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
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