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Found 8 papers, 4 papers with code
AdaptSR: Low-Rank Adaptation for Efficient and Scalable Real-World Super-Resolution
Recovering high-frequency details and textures from low-resolution images remains a fundamental challenge in super-resolution (SR), especially when real-world degradations are complex and unknown.
ContextFormer: Redefining Efficiency in Semantic Segmentation
We propose ContextFormer, a hybrid framework leveraging the strengths of CNNs and ViTs in the bottleneck to balance efficiency, accuracy, and robustness for real-time semantic segmentation.
Complexity Experts are Task-Discriminative Learners for Any Image Restoration
To address this, we introduce ``complexity experts" -- flexible expert blocks with varying computational complexity and receptive fields.
Ranked #2 on
Blind All-in-One Image Restoration
on 5-Degradations
Efficient Degradation-aware Any Image Restoration
Reconstructing missing details from degraded low-quality inputs poses a significant challenge.
Ranked #4 on
5-Degradation Blind All-in-One Image Restoration
on 5-Degradation Blind All-in-One Image Restoration
5-Degradation Blind All-in-One Image Restoration
Blind All-in-One Image Restoration
+1
Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs
Neural-based end-to-end learnable ISPs offer promising advancements, potentially replacing traditional ISPs with their ability to adapt without requiring extensive tuning for each new camera model, as is often the case for nearly every module in traditional ISPs.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
See More Details: Efficient Image Super-Resolution by Experts Mining
Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts.
Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement
Burst image processing is becoming increasingly popular in recent years.
