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Found 9 papers, 3 papers with code
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment
In this letter, we attempt to evaluate the perceptual quality and reconstruction fidelity of SR images considering LR images and scale factors.
DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual Grounding
Specifically, we propose \textbf{DARA}, a novel PETL method comprising \underline{\textbf{D}}omain-aware \underline{\textbf{A}}dapters (DA Adapters) and \underline{\textbf{R}}elation-aware \underline{\textbf{A}}dapters (RA Adapters) for VG.
Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
Contemporary recommender systems predominantly rely on collaborative filtering techniques, employing ID-embedding to capture latent associations among users and items.
Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images
Presently, the task of few-shot object detection (FSOD) in remote sensing images (RSIs) has become a focal point of attention.
VGDiffZero: Text-to-image Diffusion Models Can Be Zero-shot Visual Grounders
Large-scale text-to-image diffusion models have shown impressive capabilities for generative tasks by leveraging strong vision-language alignment from pre-training.
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.
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).
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
The pixel domain deep network takes the four downsampled versions of the compressed image to form a 4-channel input and outputs a pixel domain prediction, while the wavelet domain deep network uses the 1-level discrete wavelet transformation (DWT) coefficients to form a 4-channel input to produce a DWT domain prediction.
Ranked #7 on
JPEG Artifact Correction
on LIVE1 (Quality 20 Color)
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.
