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Found 6 papers, 6 papers with code
Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring
The CLGF module is composed of two branches: the global branch captures long-range dependency features via a selective state spaces model, while the local branch employs simplified channel attention to model local connectivity, thereby reducing local pixel forgetting and channel redundancy.
Ranked #1 on
Deblurring
on RealBlur-R (trained on GoPro)
Prompt-based Ingredient-Oriented All-in-One Image Restoration
Image restoration aims to recover the high-quality images from their degraded observations.
Ranked #1 on
Single Image Deraining
on Test1200
A Mountain-Shaped Single-Stage Network for Accurate Image Restoration
Image restoration is the task of aiming to obtain a high-quality image from a corrupt input image, such as deblurring and deraining.
Ranked #1 on
Image Deblurring
on HIDE (trained on GOPRO)
SU-Net: Pose estimation network for non-cooperative spacecraft on-orbit
In this way, the feature loss and the complexity of the model is reduced, and the degradation of deep neural network during training is avoided.
Reentry Risk and Safety Assessment of Spacecraft Debris Based on Machine Learning
Then, we compare the prediction accuracy of the three models.
Mixed Hierarchy Network for Image Restoration
Our main proposal is a mixed hierarchy architecture, that progressively recovers contextual information and spatial details from degraded images while we design intra-blocks to reduce system complexity.
Ranked #2 on
Image Deblurring
on HIDE (trained on GOPRO)
