Open AccessProceedings Article
Focal Frequency Loss for Image Reconstruction and Synthesis
Liming Jiang,Bo Dai,Wayne Wu,Chen Change Loy +3 more
- 01 Jan 2021
pp 13919-13929
TL;DR: In this article, the authors propose a novel focal frequency loss, which allows a model to adaptively focus on frequency components that are hard to synthesize by down-weighting the easy ones.
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Abstract: Image reconstruction and synthesis have witnessed remarkable progress thanks to the development of generative models. Nonetheless, gaps could still exist between the real and generated images, especially in the frequency domain. In this study, we show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further. We propose a novel focal frequency loss, which allows a model to adaptively focus on frequency components that are hard to synthesize by down-weighting the easy ones. This objective function is complementary to existing spatial losses, offering great impedance against the loss of important frequency information due to the inherent bias of neural networks. We demonstrate the versatility and effectiveness of focal frequency loss to improve popular models, such as VAE, pix2pix, and SPADE, in both perceptual quality and quantitative performance. We further show its potential on StyleGAN2.
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Citations
MAXIM: Multi-Axis MLP for Image Processing
01 Jun 2022
TL;DR: MAXIM as discussed by the authors uses a UNet-shaped hierarchical structure and supports long-range interactions enabled by spatially-gated MLPs, which can serve as an efficient and flexible general-purpose vision backbone for image processing tasks.
311
Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction
Chunming He,Kaixuan Li,Yachao Zhang,Longxiang Tang,Yulun Zhang,Zhenhua Guo,Xiu Li +6 more
- 01 Jun 2023
TL;DR: By learning the auxiliary task in conjunction with the COD task, the FEDER model can generate precise prediction maps with accurate object boundaries and significantly outperforms state-of-the-art methods with cheaper computational and memory costs.
178
Deep Fourier-Based Exposure Correction Network with Spatial-Frequency Interaction
Jie Huang,Yajing Liu,Fengmei Zhao,Jinghao Zhang,Yukun Huang,Man Zhou,Zhiwei Xiong +6 more
- 01 Jan 2022
TL;DR: Huang et al. as discussed by the authors proposed a deep Fourier-based exposure correction network (FECNet) consisting of an amplitude sub-network and a phase subnetwork to progressively reconstruct the representation of lightness and structure components.
109
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
01 Jun 2022
TL;DR: In this article , a high-resolution dual-domain learning network (HDNet) is proposed for hyperspectral image reconstruction, which combines spatial-spectral attention and frequency domain learning.
Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images
Ruicheng Wang,Jiaolong Yang +1 more
- 24 May 2022
TL;DR: This paper designs a network structure that consists of two novel modules, one for plane depth adjustment and another for depth-aware color prediction, and proposes a new method based on the multiplane image (MPI) representation for synthesizing novel views for in-the-wild photographs.
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FakeRetouch: Evading DeepFakes Detection via the Guidance of Deliberate Noise
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Spectral Distribution Aware Image Generation
Steffen Jung,Margret Keuper +1 more
- 18 May 2021
TL;DR: In this article, a spectral discriminator is proposed to generate images according to the frequency distribution of the real data by employing a GAN loss, which works stably with different commonly used GAN losses.
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SWAGAN: A Style-based Wavelet-driven Generative Model
TL;DR: In this article, a novel general-purpose Style and WAvelet based GAN (SWAGAN) is presented, which implements progressive generation in the frequency domain and incorporates wavelets throughout its generator and discriminator architectures, enforcing a frequency-aware latent representation at every step of the way.
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Convolutional Neural Network Feature Reduction using Wavelet Transform
TL;DR: Wavelet transform possible application for convolutional neural networks (CNN) is described, which can be useful for CNN input feature reduction as well as architecture simplicity by using only part of coefficients.