Deep generative approaches have obtained great success in image inpainting
recently. However, most generative inpainting networks suffer from either
over-smooth results or aliasing artifacts. The former lacks high-frequency
details, while the latter lacks semantic structure. To address this issue, we
propose an effective Frequency-Spatial Complementary Network (FSCN) by
exploiting rich semantic information in both spatial and frequency domains.
Specifically, we introduce an extra Frequency Branch and Frequency Loss on the
spatial-based network to impose direct supervision on the frequency
information, and propose a Frequency-Spatial Cross-Attention Block (FSCAB) to
fuse multi-domain features and combine the corresponding characteristics. With
our FSCAB, the inpainting network is capable of capturing frequency information
and preserving visual consistency simultaneously. Extensive quantitative and
qualitative experiments demonstrate that our inpainting network can effectively
achieve superior results, outperforming previous state-of-the-art approaches
with significantly fewer parameters and less computation cost. The code will be
released soon.

该研究提出了一种有效的频空间补偿网络 (Frequency-Spatial Complementary Network)，通过利用空间和频率域中丰富的语义信息建立了额外的频率支路和频率损失，并提出了一种频率 - 空间跨域注意力块 (Frequency-Spatial Cross-Attention Block) 来融合多域特征和结合对应的特征。通过测试和量化实验，证明了该方法在保留高频细节、语义结构和视觉一致性方面表现优异。

空间和频率提示对高保真图像修复的贡献

Both Spatial and Frequency Cues Contribute to High-Fidelity Image  Inpainting

Haze usually leads to deteriorated images with low contrast, color shift and
structural distortion. We observe that many deep learning based models exhibit
exceptional performance on removing homogeneous haze, but they usually fail to
address the challenge of non-homogeneous dehazing. Two main factors account for
this situation. Firstly, due to the intricate and non uniform distribution of
dense haze, the recovery of structural and chromatic features with high
fidelity is challenging, particularly in regions with heavy haze. Secondly, the
existing small scale datasets for non-homogeneous dehazing are inadequate to
support reliable learning of feature mappings between hazy images and their
corresponding haze-free counterparts by convolutional neural network
(CNN)-based models. To tackle these two challenges, we propose a novel two
branch network that leverages 2D discrete wavelete transform (DWT), fast
Fourier convolution (FFC) residual block and a pretrained ConvNeXt model.
Specifically, in the DWT-FFC frequency branch, our model exploits DWT to
capture more high-frequency features. Moreover, by taking advantage of the
large receptive field provided by FFC residual blocks, our model is able to
effectively explore global contextual information and produce images with
better perceptual quality. In the prior knowledge branch, an ImageNet
pretrained ConvNeXt as opposed to Res2Net is adopted. This enables our model to
learn more supplementary information and acquire a stronger generalization
ability. The feasibility and effectiveness of the proposed method is
demonstrated via extensive experiments and ablation studies. The code is
available at this https URL

该研究提出了一种新的两分支网络，利用 2D 离散小波变换、快速傅里叶卷积残差块和预训练的 ConvNeXt 模型来解决雾霾图像非均质去雾的问题，并进行了大量实验验证其有效性。