Single Image Desnowing
7 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Restormer: Efficient Transformer for High-Resolution Image Restoration
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Simple Baselines for Image Restoration
Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods.
Uformer: A General U-Shaped Transformer for Image Restoration
Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-Tree Complex Wavelet Representation and Contradict Channel Loss
Moreover, due to the limitation of existing snow datasets, to simulate the snow scenarios comprehensively, we propose a large-scale dataset called Comprehensive Snow Dataset (CSD).
TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions
We also introduce a transformer decoder with learnable weather type embeddings to adjust to the weather degradation at hand.
SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing
Due to various and complicated snow degradations, single image desnowing is a challenging image restoration task.
Exploring the potential of channel interactions for image restoration
Image restoration aims to reconstruct a clear image from a degraded observation.