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Found 30 papers, 13 papers with code
Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects
Significantly, such confidence score learned from class-known objects can be generalized to unknown ones.
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.
Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration
In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions.
Deformable Cross-Attention Transformer for Medical Image Registration
Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration.
Automated Ventricle Parcellation and Evan's Ratio Computation in Pre- and Post-Surgical Ventriculomegaly
Normal pressure hydrocephalus~(NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms.
A latent space for unsupervised MR image quality control via artifact assessment
Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images.
DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue
However, this technique faces several challenges such as tag fading, large motion, long computation times, and difficulties in obtaining diffeomorphic incompressible flow fields.
On Finite Difference Jacobian Computation in Deformable Image Registration
We show that for a 2D transformation, four unique finite difference approximations of $|J|$'s must be positive to ensure the entire domain is invertible and free of folding at the pixel level.
HACA3: A Unified Approach for Multi-site MR Image Harmonization
HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts.
Cross-identity Video Motion Retargeting with Joint Transformation and Synthesis
The novel design of dual branches combines the strengths of deformation-grid-based transformation and warp-free generation for better identity preservation and robustness to occlusion in the synthesized videos.
CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm
Inspired by prompting approaches from NLP, we creatively reinterpret point cloud generation and refinement as the prompting and predicting stages, respectively.
Evaluating the Generalization Ability of Super-Resolution Networks
However, research on the generalization ability of Super-Resolution (SR) networks is currently absent.
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds
The recently proposed practical degradation model includes a full spectrum of degradation types, but only considers complex cases that use all degradation types in the degradation process, while ignoring many important corner cases that are common in the real world.
Disentangling A Single MR Modality
Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks.
Coordinate Translator for Learning Deformable Medical Image Registration
The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images.
Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning
We address this problem from a new perspective, by jointly considering colorization and temporal consistency in a unified framework.
Learn to Match: Automatic Matching Network Design for Visual Tracking
Siamese tracking has achieved groundbreaking performance in recent years, where the essence is the efficient matching operator cross-correlation and its variants.
Discovering Distinctive "Semantics" in Super-Resolution Networks
We show that a well-trained deep SR network is naturally a good descriptor of degradation information.
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank
To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of different perceptual metrics.
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.
Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings
In this paper, we present a fast exemplar-based image colorization approach using color embeddings named Color2Embed.
HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization
In this work, we propose a novel learning-based approach using a spatially dynamic encoder-decoder network, HDRUNet, to learn an end-to-end mapping for single image HDR reconstruction with denoising and dequantization.
Very Lightweight Photo Retouching Network with Conditional Sequential Modulation
In practice, photo retouching can be accomplished by a series of image processing operations.
Information-based Disentangled Representation Learning for Unsupervised MR Harmonization
Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis.
Conditional Sequential Modulation for Efficient Global Image Retouching
The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector.
Enhanced Quadratic Video Interpolation
In this work, we further improve the performance of QVI from three facets and propose an enhanced quadratic video interpolation (EQVI) model.
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing
With the proposed Fusion-discriminator which takes frequency information as additional priors, our model can generator more natural and realistic dehazed images with less color distortion and fewer artifacts.
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of perceptual metrics.
Ranked #1 on
Image Super-Resolution
on PIRM-test
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
