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Found 16 papers, 6 papers with code
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.
Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks
In the rapidly advancing field of federated learning (FL), ensuring efficient FL task delegation while incentivising FL client participation poses significant challenges, especially in wireless networks where FL participants' coverage is limited.
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
It assigns a shared homogeneous small feature extractor and a local gating network for each client's local heterogeneous large model.
Multi-task Image Restoration Guided By Robust DINO Features
Multi-task image restoration has gained significant interest due to its inherent versatility and efficiency compared to its single-task counterpart.
Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches
Although unsupervised approaches based on generative adversarial networks offer a promising solution for denoising without paired datasets, they are difficult in surpassing the performance limitations of conventional GAN-based unsupervised frameworks without significantly modifying existing structures or increasing the computational complexity of denoisers.
Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images
Structural MRI and PET imaging play an important role in the diagnosis of Alzheimer's disease (AD), showing the morphological changes and glucose metabolism changes in the brain respectively.
Random Sub-Samples Generation for Self-Supervised Real Image Denoising
With sufficient paired training samples, the supervised deep learning methods have attracted much attention in image denoising because of their superior performance.
Towards Quantum Federated Learning
This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.
Unlocking Low-Light-Rainy Image Restoration by Pairwise Degradation Feature Vector Guidance
P-Net can learn degradation feature vectors on the dark and light areas separately, using contrastive learning to guide the image restoration process.
Geometry and Uncertainty-Aware 3D Point Cloud Class-Incremental Semantic Segmentation
Despite the significant recent progress made on 3D point cloud semantic segmentation, the current methods require training data for all classes at once, and are not suitable for real-life scenarios where new categories are being continuously discovered.
Adaptive Consistency Prior Based Deep Network for Image Denoising
To solve this problem, we propose a novel model-based denoising method to inform the design of our denoising network.
Multi-resolution Outlier Pooling for Sorghum Classification
Automated high throughput plant phenotyping involves leveraging sensors, such as RGB, thermal and hyperspectral cameras (among others), to make large scale and rapid measurements of the physical properties of plants for the purpose of better understanding the difference between crops and facilitating rapid plant breeding programs.
Real-World Single Image Super-Resolution: A Brief Review
More specifically, this review covers the critical publically available datasets and assessment metrics for RSISR, and four major categories of RSISR methods, namely the degradation modeling-based RSISR, image pairs-based RSISR, domain translation-based RSISR, and self-learning-based RSISR.
Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis
Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making.
Accurate and Fast reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Network
To overcome this shortcoming, in this study we proposed a deep learning-based framework for reconstructing full image from its much smaller sub-area(s).
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
Experiments on compressed images produced by JPEG (we take the JPEG as an example in this paper) demonstrate that the proposed CISRDCNN yields state-of-the-art SR performance on commonly used test images and imagesets.
