2,278 research outputs found
Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints
This paper presents a significant improvement for the synthesis of texture
images using convolutional neural networks (CNNs), making use of constraints on
the Fourier spectrum of the results. More precisely, the texture synthesis is
regarded as a constrained optimization problem, with constraints conditioning
both the Fourier spectrum and statistical features learned by CNNs. In contrast
with existing methods, the presented method inherits from previous CNN
approaches the ability to depict local structures and fine scale details, and
at the same time yields coherent large scale structures, even in the case of
quasi-periodic images. This is done at no extra computational cost. Synthesis
experiments on various images show a clear improvement compared to a recent
state-of-the art method relying on CNN constraints only
A survey of exemplar-based texture synthesis
Exemplar-based texture synthesis is the process of generating, from an input
sample, new texture images of arbitrary size and which are perceptually
equivalent to the sample. The two main approaches are statistics-based methods
and patch re-arrangement methods. In the first class, a texture is
characterized by a statistical signature; then, a random sampling conditioned
to this signature produces genuinely different texture images. The second class
boils down to a clever "copy-paste" procedure, which stitches together large
regions of the sample. Hybrid methods try to combine ideas from both approaches
to avoid their hurdles. The recent approaches using convolutional neural
networks fit to this classification, some being statistical and others
performing patch re-arrangement in the feature space. They produce impressive
synthesis on various kinds of textures. Nevertheless, we found that most real
textures are organized at multiple scales, with global structures revealed at
coarse scales and highly varying details at finer ones. Thus, when confronted
with large natural images of textures the results of state-of-the-art methods
degrade rapidly, and the problem of modeling them remains wide open.Comment: v2: Added comments and typos fixes. New section added to describe
FRAME. New method presented: CNNMR
Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss
In the past decade, exemplar-based texture synthesis algorithms have seen
strong gains in performance by matching statistics of deep convolutional neural
networks. However, these algorithms require regularization terms or user-added
spatial tags to capture long range constraints in images. Having access to a
user-added spatial tag for all situations is not always feasible, and
regularization terms can be difficult to tune. It would be ideal to create an
algorithm that does not have any of the aforementioned drawbacks. Thus, we
propose a new set of statistics for exemplar based texture synthesis based on
Sliced Wasserstein Loss and create a multi-scale algorithm to synthesize
textures without a user-added spatial tag. Lastly, we study the ability of our
proposed algorithm to capture long range constraints in images and compare our
results to other exemplar-based neural texture synthesis algorithms.Comment: Submitted to IEEE for possible publicatio
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