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Found 26 papers, 23 papers with code
Improving Image Restoration through Removing Degradations in Textual Representations
To address the cross-modal assistance, we propose to map the degraded images into textual representations for removing the degradations, and then convert the restored textual representations into a guidance image for assisting image restoration.
Aggregating Long-term Sharp Features via Hybrid Transformers for Video Deblurring
To aggregate long-term sharp features from detected sharp frames, we utilize a global Transformer with multi-scale matching capability.
Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive
In this paper, we propose a Self-supervised learning framework for Dual reversed RS distortions Correction (SelfDRSC), where a DRSC network can be learned to generate a high framerate GS video only based on dual RS images with reversed distortions.
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1. 5 dB in terms of PSNR.
Learning Single Image Defocus Deblurring with Misaligned Training Pairs
First, in the deblurring module, a bi-directional optical flow-based deformation is introduced to tolerate spatial misalignment between deblurred and ground-truth images.
Robust Deep Ensemble Method for Real-world Image Denoising
In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps to fuse these denoisers.
Learning Dual-Pixel Alignment for Defocus Deblurring
Generally, DPANet is an encoder-decoder with skip-connections, where two branches with shared parameters in the encoder are employed to extract and align deep features from left and right views, and one decoder is adopted to fuse aligned features for predicting the sharp image.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment
Particularly, by treating all labeled data as positive samples, PU learning is leveraged to identify negative samples (i. e., outliers) from unlabeled data.
Localization Distillation for Object Detection
Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and the absence of localization distillation is a critical reason for why logit mimicking underperforms for years.
Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation
For learning pseudo mask generator from the auxiliary dataset, we present a bi-level optimization formulation.
Localization Distillation for Dense Object Detection
Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement.
Bringing Events Into Video Deblurring With Non-Consecutively Blurry Frames
EFM can also be easily incorporated into existing deblurring networks, making event-driven deblurring task benefit from state-of-the-art deblurring methods.
Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance
To be specific, the reflection layer is firstly estimated due to that it generally is much simpler and is relatively easier to estimate.
Unpaired Learning of Deep Image Denoising
As for knowledge distillation, we first apply the learned noise models to clean images to synthesize a paired set of training images, and use the real noisy images and the corresponding denoising results in the first stage to form another paired set.
Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation
In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective
Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification task.
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
By incorporating DIoU and CIoU losses into state-of-the-art object detection algorithms, e. g., YOLO v3, SSD and Faster RCNN, we achieve notable performance gains in terms of not only IoU metric but also GIoU metric.
Neural Blind Deconvolution Using Deep Priors
To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution.
STAR: A Structure and Texture Aware Retinex Model
A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image.
Progressive Image Deraining Networks: A Better and Simpler Baseline
To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions.
Ranked #1 on
Single Image Deraining
on Rain1400
Unsupervised Degradation Learning for Single Image Super-Resolution
Specifically, we propose a degradation network to model the real-world degradation process from HR to LR via generative adversarial networks, and these generated realistic LR images paired with real-world HR images are exploited for training the SR reconstruction network, forming the first cycle.
Simultaneous Fidelity and Regularization Learning for Image Restoration
For blind deconvolution, as estimation error of blur kernel is usually introduced, the subsequent non-blind deconvolution process does not restore the latent image well.
Understanding Kernel Size in Blind Deconvolution
In this paper, we first theoretically and experimentally analyze the mechanism to estimation error in oversized kernel, and show that it holds even on blurry images without noises.
Discriminative Learning of Iteration-Wise Priors for Blind Deconvolution
The maximum a posterior (MAP)-based blind deconvolution framework generally involves two stages: blur kernel estimation and non-blind restoration.
