Texture synthesis via projection onto multiscale, multilayer statistics
Jieqian He, Matthew Hirn

TL;DR
This paper introduces a novel texture synthesis method using multiscale, multilayer feature extraction with wavelet coefficients, enabling high-quality texture generation and insights into deep image representations.
Contribution
It presents a new model combining wavelet-based features and multilayer structures for improved texture synthesis and analysis.
Findings
Generated high-quality textures
Demonstrated advantages of multilayer features
Provided insights into deep texture representations
Abstract
We provide a new model for texture synthesis based on a multiscale, multilayer feature extractor. Within the model, textures are represented by a set of statistics computed from ReLU wavelet coefficients at different layers, scales and orientations. A new image is synthesized by matching the target statistics via an iterative projection algorithm. We explain the necessity of the different types of pre-defined wavelet filters used in our model and the advantages of multilayer structures for image synthesis. We demonstrate the power of our model by generating samples of high quality textures and providing insights into deep representations for texture images.
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Image Retrieval and Classification Techniques · Computer Graphics and Visualization Techniques
