Shadow-affected images often exhibit pronounced spatial discrepancies in
color and illumination, consequently degrading various vision applications
including object detection and segmentation systems. To effectively eliminate
shadows in real-world images while preserving intricate details and producing
visually compelling outcomes, we introduce a mask-free Shadow Removal and
Refinement network (ShadowRefiner) via Fast Fourier Transformer. Specifically,
the Shadow Removal module in our method aims to establish effective mappings
between shadow-affected and shadow-free images via spatial and frequency
representation learning. To mitigate the pixel misalignment and further improve
the image quality, we propose a novel Fast-Fourier Attention based Transformer
(FFAT) architecture, where an innovative attention mechanism is designed for
meticulous refinement. Our method wins the championship in the Perceptual Track
and achieves the second best performance in the Fidelity Track of NTIRE 2024
Image Shadow Removal Challenge. Besides, comprehensive experiment result also
demonstrate the compelling effectiveness of our proposed method. The code is
publicly available: this https URL

通过 Fast Fourier Transformer 引入一个无需遮罩的 Shadow Removal and Refinement 网络 (ShadowRefiner)，以实现对真实世界图像中的阴影进行有效去除并保留细节和产生具有视觉吸引力的结果。

ShadowRefiner：基于快速傅立叶变换的无掩模去除阴影方法

ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier  Transformer

This study focuses on multi-step spatio-temporal wind speed forecasting for
the Norwegian continental shelf. The study aims to leverage spatial
dependencies through the relative physical location of different measurement
stations to improve local wind forecasts. Our multi-step forecasting models
produce either 10-minute, 1- or 4-hour forecasts, with 10-minute resolution,
meaning that the models produce more informative time series for predicted
future trends. A graph neural network (GNN) architecture was used to extract
spatial dependencies, with different update functions to learn temporal
correlations. These update functions were implemented using different neural
network architectures. One such architecture, the Transformer, has become
increasingly popular for sequence modelling in recent years. Various
alterations have been proposed to better facilitate time series forecasting, of
which this study focused on the Informer, LogSparse Transformer and Autoformer.
This is the first time the LogSparse Transformer and Autoformer have been
applied to wind forecasting and the first time any of these or the Informer
have been formulated in a spatio-temporal setting for wind forecasting. By
comparing against spatio-temporal Long Short-Term Memory (LSTM) and Multi-Layer
Perceptron (MLP) models, the study showed that the models using the altered
Transformer architectures as update functions in GNNs were able to outperform
these. Furthermore, we propose the Fast Fourier Transformer (FFTransformer),
which is a novel Transformer architecture based on signal decomposition and
consists of two separate streams that analyse the trend and periodic components
separately. The FFTransformer and Autoformer were found to achieve superior
results for the 10-minute and 1-hour ahead forecasts, with the FFTransformer
significantly outperforming all other models for the 4-hour ahead forecasts.

本研究使用图神经网络和不同的 Transformer 架构，探讨风速预测的时空依赖性，提出了 Fast Fourier Transformer 和 Autoformer 架构，使用这些架构的模型在时空预测上的表现优于其他模型。