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Found 44 papers, 23 papers with code
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning
Leveraging unseen LR images for self-supervised learning guides the model to adapt its modeling space to the target domain, facilitating fine-tuning of SR models without requiring paired high-resolution (HR) images.
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up.
Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models
We introduce a Depicted image Quality Assessment method (DepictQA), overcoming the constraints of traditional score-based methods.
Image Super-Resolution with Text Prompt Diffusion
Specifically, we first design a text-image generation pipeline to integrate text into the SR dataset through the text degradation representation and degradation model.
Binarized 3D Whole-body Human Mesh Recovery
In this work, we propose a Binarized Dual Residual Network (BiDRN), a novel quantization method to estimate the 3D human body, face, and hands parameters efficiently.
Dual Aggregation Transformer for Image Super-Resolution
Based on the above idea, we propose a novel Transformer model, Dual Aggregation Transformer (DAT), for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner.
Ranked #6 on
Image Super-Resolution
on Manga109 - 4x upscaling
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution
In this work, we introduce a novel approach to craft training degradation distributions using a small set of reference images.
Hierarchical Integration Diffusion Model for Realistic Image Deblurring
Specifically, we perform the DM in a highly compacted latent space to generate the prior feature for the deblurring process.
Masked Image Training for Generalizable Deep Image Denoising
To address this issue, we present a novel approach to enhance the generalization performance of denoising networks, known as masked training.
Recursive Generalization Transformer for Image Super-Resolution
In this work, we propose the Recursive Generalization Transformer (RGT) for image SR, which can capture global spatial information and is suitable for high-resolution images.
Ranked #5 on
Image Super-Resolution
on Manga109 - 4x upscaling
Xformer: Hybrid X-Shaped Transformer for Image Denoising
The channel-wise Transformer block performs direct global context interactions across tokens defined by channel dimension.
DegAE: A New Pretraining Paradigm for Low-Level Vision
However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging.
Snow Removal in Video: A New Dataset and A Novel Method
We also develop a deep learning framework for video snow removal.
Mitigating Artifacts in Real-World Video Super-Resolution Models
To aggregate a new hidden state that contains fewer artifacts from the hidden state pool, we devise a Selective Cross Attention (SCA) module, in which the attention between input features and each hidden state is calculated.
Cross Aggregation Transformer for Image Restoration
The core of our CAT is the Rectangle-Window Self-Attention (Rwin-SA), which utilizes horizontal and vertical rectangle window attention in different heads parallelly to expand the attention area and aggregate the features cross different windows.
Self-Supervised Intensity-Event Stereo Matching
Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption.
Efficient Image Super-Resolution using Vast-Receptive-Field Attention
In this work, we design an efficient SR network by improving the attention mechanism.
Near Real-time CO$_2$ Emissions Based on Carbon Satellite and Artificial Intelligence
Here we provide the first method that combines the advanced artificial intelligence (AI) techniques and the carbon satellite monitor to quantify anthropogenic CO$_2$ emissions.
Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images
We finally use a guided fusion operation to integrate the sharp edges generated by the network and flat areas by the interpolation method to get the final SR image.
Accurate Image Restoration with Attention Retractable Transformer
This is considered as a dense attention strategy since the interactions of tokens are restrained in dense regions.
Rethinking Alignment in Video Super-Resolution Transformers
In this paper, we rethink the role of alignment in VSR Transformers and make several counter-intuitive observations.
Ranked #2 on
Video Super-Resolution
on Vid4 - 4x upscaling
NTIRE 2022 Challenge on Perceptual Image Quality Assessment
This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods.
Evaluating the Generalization Ability of Super-Resolution Networks
However, research on the generalization ability of Super-Resolution (SR) networks is currently absent.
Blueprint Separable Residual Network for Efficient Image Super-Resolution
One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Texture-Based Error Analysis for Image Super-Resolution
Our key contribution is to leverage a texture classifier, which enables us to assign patches with semantic labels, to identify the source of SR errors both globally and locally.
Reflash Dropout in Image Super-Resolution
Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR).
Discovering Distinctive "Semantics" in Super-Resolution Networks
We show that a well-trained deep SR network is naturally a good descriptor of degradation information.
Blind Image Super-Resolution: A Survey and Beyond
This paper serves as a systematic review on recent progress in blind image SR, and proposes a taxonomy to categorize existing methods into three different classes according to their ways of degradation modelling and the data used for solving the SR model.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Attention in Attention Network for Image Super-Resolution
In this work, we attempt to quantify and visualize attention mechanisms in SISR and show that not all attention modules are equally beneficial.
Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric
To answer the questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing ALgorithms (PIPAL) dataset.
Interpreting Super-Resolution Networks with Local Attribution Maps
Based on LAM, we show that: (1) SR networks with a wider range of involved input pixels could achieve better performance.
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details.
PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration
To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset.
Image Processing Using Multi-Code GAN Prior
Such an over-parameterization of the latent space significantly improves the image reconstruction quality, outperforming existing competitors.
Ranked #7 on
Blind Face Restoration
on CelebA-Test
Interpreting the Latent Space of GANs for Semantic Face Editing
In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs.
Suppressing Model Overfitting for Image Super-Resolution Networks
Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved.
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions.
Blind Super-Resolution With Iterative Kernel Correction
In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown.
Ranked #2 on
Blind Super-Resolution
on Set5 - 3x upscaling
Two-phase Hair Image Synthesis by Self-Enhancing Generative Model
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs).
Super-Resolution Perception for Industrial Sensor Data
In this paper, we present the problem formulation and methodology framework of Super-Resolution Perception (SRP) on industrial sensor data.
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).
Ranked #2 on
Face Hallucination
on FFHQ 512 x 512 - 16x upscaling
Single Image Reflection Removal Using Deep Encoder-Decoder Network
Image of a scene captured through a piece of transparent and reflective material, such as glass, is often spoiled by a superimposed layer of reflection image.
