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Propagation of Waves

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When a wave propagates from one medium to another, part of it may get reflected in the first medium, and part of it may get transmitted to the second medium. In such a case, the interface of the two mediums can be considered as a boundary that is neither fixed nor free.
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Updated: Oct 13, 2025

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

Fractional Wavelet-Based Generative Scattering Networks.

Jiasong Wu^1,2,3,4, Xiang Qiu^1,4, Jing Zhang^1,4

¹Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China.

Frontiers in Neurorobotics

|November 12, 2021

Summary

This summary is machine-generated.

Generative fractional scattering networks (GFRSNs) improve image generation by using more expressive encoders and a novel feature-map fusion method, outperforming existing generative scattering networks (GSNs). This addresses training instability in generative models.

Keywords:

feature-map fusion fractional wavelet scattering network generative model image fusion image generation

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Last Updated: Oct 13, 2025

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Area of Science:

Machine Learning
Computer Vision
Deep Learning

Background:

Generative adversarial networks (GANs) and variational autoencoders (VAEs) face training instability due to simultaneous training of generator/encoder and discriminator/decoder.
Generative scattering networks (GSNs) use wavelet scattering networks (ScatNets) as encoders and convolutional neural networks (CNNs) as decoders, offering advantages but with limitations in representation power and dimensionality reduction.

Purpose of the Study:

To enhance image generation quality by improving upon Generative Scattering Networks (GSNs).
To address limitations of ScatNets and Principal Component Analysis (PCA) in GSNs for better feature representation and reduced overfitting.

Main Methods:

Proposed Generative Fractional Scattering Networks (GFRSNs) utilizing fractional wavelet scattering networks (FrScatNets) as encoders for more expressive feature extraction.
Introduced a new dimensionality reduction technique, feature-map fusion (FMF), to preserve information from FrScatNets, replacing Principal Component Analysis (PCA).
Evaluated GFRSNs on CIFAR-10 and CelebA datasets, comparing performance against GSNs and integrating with Deep Convolutional GAN (DCGAN), Progressive GAN (PGAN), and CycleGAN.

Main Results:

GFRSNs demonstrated superior image generation quality on testing datasets compared to traditional GSNs.
The proposed feature-map fusion (FMF) method effectively retained information, mitigating overfitting issues associated with PCA.
Experimental results confirmed the effectiveness of GFRSNs when integrated with various GAN architectures.

Conclusions:

Generative Fractional Scattering Networks (GFRSNs) offer a promising advancement for stable and high-quality image generation.
The combination of FrScatNets and FMF provides a robust framework for generative modeling, outperforming previous approaches.
The study highlights the potential of GFRSNs for diverse applications in generative AI and computer vision.