Search Results for author:
Found 115 papers, 80 papers with code
LatticeNet: Towards Lightweight Image Super-resolution with Lattice Block
Drawing lessons from lattice filter bank, we design the lattice block (LB) in which two butterfly structures are applied to combine two RBs.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Binarized Low-light Raw Video Enhancement
For the second issue, we present a distribution-aware binary convolution, which captures the distribution characteristics of real-valued input and incorporates them into plain binary convolutions to alleviate the degradation in performance.
PASTA: Towards Flexible and Efficient HDR Imaging Via Progressively Aggregated Spatio-Temporal Alignment
Leveraging Transformer attention has led to great advancements in HDR deghosting.
Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal
Although ViWS-Net is proposed to remove adverse weather conditions in videos with a single set of pre-trained weights, it is seriously blinded by seen weather at train-time and degenerates when coming to unseen weather during test-time.
Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
X-ray is widely applied for transmission imaging due to its stronger penetration than natural light.
See More Details: Efficient Image Super-Resolution by Experts Mining
Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts.
Image Fusion via Vision-Language Model
Therefore, we introduce a novel fusion paradigm named image Fusion via vIsion-Language Model (FILM), for the first time, utilizing explicit textual information in different source images to guide image fusion.
Guidance Graph Optimization for Lifelong Multi-Agent Path Finding
Empirically, we show that (1) our guidance graphs improve the throughput of three representative lifelong MAPF algorithms in four benchmark maps, and (2) our update model can generate guidance graphs for as large as $93 \times 91$ maps and as many as 3000 agents.
Scalable Mechanism Design for Multi-Agent Path Finding
In this work, we introduce the problem of scalable mechanism design for MAPF and propose three strategyproof mechanisms, two of which even use approximate MAPF algorithms.
DPoser: Diffusion Model as Robust 3D Human Pose Prior
Our approach demonstrates considerable enhancements over common uniform scheduling used in image domains, boasting improvements of 5. 4%, 17. 2%, and 3. 8% across human mesh recovery, pose completion, and motion denoising, respectively.
MuRF: Multi-Baseline Radiance Fields
We present Multi-Baseline Radiance Fields (MuRF), a general feed-forward approach to solving sparse view synthesis under multiple different baseline settings (small and large baselines, and different number of input views).
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.
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.
Deep Equilibrium Diffusion Restoration with Parallel Sampling
Due to the inherent property of diffusion models, most existing methods need long serial sampling chains to restore HQ images step-by-step, resulting in expensive sampling time and high computation costs.
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model
These priors are subsequently utilized by RGformer to guide the decomposition of image features into their respective reflectance and illumination components.
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2, 350 robots.
OHQ: On-chip Hardware-aware Quantization
Mixed-precision quantization leverages multiple bit-width architectures to unleash the accuracy and efficiency potential of quantized models.
Neural Gradient Regularizer
NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer.
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation
Compared to traditional DMs, the compact IPR enables DiffI2I to obtain more accurate outcomes and employ a lighter denoising network and fewer iterations.
Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image Reconstruction
3) stage interaction ignoring the differences in features at different stages.
Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing
Multi-stage architectures have exhibited efficacy in image dehazing, which usually decomposes a challenging task into multiple more tractable sub-tasks and progressively estimates latent hazy-free images.
Recurrent Self-Supervised Video Denoising with Denser Receptive Field
However, under their blind spot constraints, previous self-supervised video denoising methods suffer from significant information loss and texture destruction in either the whole reference frame or neighbor frames, due to their inadequate consideration of the receptive field.
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
Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects
On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect.
Consistency Regularization for Generalizable Source-free Domain Adaptation
In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module.
Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher
This paper aims to address these two issues by proposing the Class-Balanced Mean Teacher (CBMT) model.
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging
In this work, we tackle this challenge by marrying prompt tuning with FL to snapshot compressive imaging for the first time and propose an federated hardware-prompt learning (FedHP) method.
Alignment-free HDR Deghosting with Semantics Consistent Transformer
The spatial attention aims to deal with the intra-image correlation to model the dynamic motion, while the channel attention enables the inter-image intertwining to enhance the semantic consistency across frames.
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.
Multi-Modal Mutual Attention and Iterative Interaction for Referring Image Segmentation
However, the generic attention mechanism in Transformer only uses the language input for attention weight calculation, which does not explicitly fuse language features in its output.
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.
Equivariant Multi-Modality Image Fusion
These components enable the net training to follow the principles of the natural sensing-imaging process while satisfying the equivariant imaging prior.
Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping
It remains a challenging task since (1) it is hard to distinguish concealed objects from the background due to the intrinsic similarity and (2) the sparsely-annotated training data only provide weak supervision for model learning.
Binarized Spectral Compressive Imaging
Finally, our BiSRNet is derived by using the proposed techniques to binarize the base model.
Multi-Robot Coordination and Layout Design for Automated Warehousing
We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability.
Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising
In this paper, we address these issues by proposing a spectral enhanced rectangle Transformer, driving it to explore the non-local spatial similarity and global spectral low-rank property of HSIs.
LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising
Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correlation, including the advanced self-supervised blind-spot networks (BSNs).
DiffIR: Efficient Diffusion Model for Image Restoration
Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis process into a sequential application of a denoising network.
Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
Second, existing pruning methods generally operate upon a pre-trained network for the sparse structure determination, hard to get rid of dense model training in the traditional SR paradigm.
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Then, the extracted features are mapped to the spherical space to complete the separation of private features and the alignment of shared features.
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
To leverage strong generative priors and address challenges such as unstable training and lack of interpretability for GAN-based generative methods, we propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM).
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
When enhancing low-light images, many deep learning algorithms are based on the Retinex theory.
Ranked #1 on
Low-Light Image Enhancement
on SDSD-outdoor
Low-light Image Deblurring and Enhancement
Low-Light Image Enhancement
+3
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.
pyribs: A Bare-Bones Python Library for Quality Diversity Optimization
Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutions to a given problem.
No One Left Behind: Real-World Federated Class-Incremental Learning
These issues render global model to undergo catastrophic forgetting on old categories, when local clients receive new categories consecutively under limited memory of storing old categories.
Camouflaged Object Detection With Feature Decomposition and Edge Reconstruction
COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background, as well as their ambiguous boundaries.
Degradation-Resistant Unfolding Network for Heterogeneous Image Fusion
Heterogeneous image fusion (HIF) techniques aim to enhance image quality by merging complementary information from images captured by different sensors.
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution
We explicitly design an implicit attention network to learn the ensemble weights for the nearby local features.
Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
It consists of a knowledge distillation based implicit degradation estimator network (KD-IDE) and an efficient SR network.
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion
We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information.
Spatial-Spectral Transformer for Hyperspectral Image Denoising
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications.
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.
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.
Basic Binary Convolution Unit for Binarized Image Restoration Network
In this study, we reconsider components in binary convolution, such as residual connection, BatchNorm, activation function, and structure, for IR tasks.
S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction
By observing this physical encoding procedure, two major challenges stand in the way of a high-fidelity reconstruction.
Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising
To this end, we propose a new multi-scale refined optical flow-guided video denoising method, which is more robust to different noise levels.
Ranked #1 on
Video Denoising
on VideoLQ
Meta-Learning based Degradation Representation for Blind Super-Resolution
The most of CNN based super-resolution (SR) methods assume that the degradation is known (\eg, bicubic).
Reference-based Image Super-Resolution with Deformable Attention Transformer
Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images.
Towards Interpretable Video Super-Resolution via Alternating Optimization
These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences.
Structured Sparsity Learning for Efficient Video Super-Resolution
In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks.
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a compressed measurement.
Ranked #1 on
Spectral Reconstruction
on Real HSI
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
On the other hand, we equip the sequence-to-sequence model with an unsupervised optical flow estimator to maximize its potential.
Ranked #2 on
Video Enhancement
on MFQE v2
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI).
Ranked #1 on
Spectral Reconstruction
on ARAD-1K
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
Additionally, for better noise fitting, we present an efficient architecture Simple Multi-scale Network (SMNet) as the generator.
Ranked #1 on
Noise Estimation
on SIDD
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution
Improving the resolution of magnetic resonance (MR) image data is critical to computer-aided diagnosis and brain function analysis.
Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved.
Ranked #1 on
Image Denoising
on urban100 sigma15
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution
In this paper, we propose a novel Hybrid Pixel-Unshuffled Network (HPUN) by introducing an efficient and effective downsampling module into the SR task.
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction
Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i. e., recovering the 3D hyperspectral images (HSIs) from a 2D compressive measurement.
Ranked #2 on
Spectral Reconstruction
on Real HSI
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features.
Ranked #5 on
Spectral Reconstruction
on Real HSI
Revisiting RCAN: Improved Training for Image Super-Resolution
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight.
Flow-Guided Sparse Transformer for Video Deblurring
Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring.
Ranked #1 on
Deblurring
on DVD
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.
Modeling Mask Uncertainty in Hyperspectral Image Reconstruction
Recently, hyperspectral imaging (HSI) has attracted increasing research attention, especially for the ones based on a coded aperture snapshot spectral imaging (CASSI) system.
Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding
In a Hearthstone deckbuilding case study, we show that our approach improves the sample efficiency of MAP-Elites and outperforms a model trained offline with random decks, as well as a linear surrogate model baseline, setting a new state-of-the-art for quality diversity approaches in automated Hearthstone deckbuilding.
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation
For slow learning of graph similarity, this paper proposes a novel early-fusion approach by designing a co-attention-based feature fusion network on multilevel GNN features.
Aligned Structured Sparsity Learning for Efficient Image Super-Resolution
To address the above issues, we propose aligned structured sparsity learning (ASSL), which introduces a weight normalization layer and applies $L_2$ regularization to the scale parameters for sparsity.
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
The HSI representations are highly similar and correlated across the spectral dimension.
Ranked #2 on
Spectral Reconstruction
on ARAD-1K
A Simple Approach to Continual Learning by Transferring Skill Parameters
We take a fresh look at this problem, by considering a setting in which the robot is limited to storing that knowledge and experience only in the form of learned skill policies.
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning
Specifically, for the layers connected by the same residual, we select the filters of the same indices as unimportant filters.
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning
In this paper, we propose a novel framework, MemREIN, which considers Memorized, Restitution, and Instance Normalization for cross-domain few-shot learning.
A NEW BACKBONE FOR HYPERSPECTRAL IMAGE RECONSTRUCTION
As the inverse process of snapshot compressive imaging, the hyperspectral image (HSI) reconstruction takes the 2D measurement as input and posteriorly retrieves the captured 3D spatial-spectral signal.
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging
The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way.
Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning
We explore possible methods for multi-task transfer learning which seek to exploit the shared physical structure of robotics tasks.
Rethinking Adam: A Twofold Exponential Moving Average Approach
The intuition is that gradient with momentum contains more accurate directional information and therefore its second moment estimation is a more favorable option for learning rate scaling than that of the raw gradient.
On the Importance of Environments in Human-Robot Coordination
When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks.
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network
They also fail to sense the entire space of the input, which is critical for high-quality MR image SR. To address those problems, we propose squeeze and excitation reasoning attention networks (SERAN) for accurate MR image SR. We propose to squeeze attention from global spatial information of the input and obtain global descriptors.
Ranked #2 on
Image Super-Resolution
on IXI
Pseudo 3D Auto-Correlation Network for Real Image Denoising
The extraction of auto-correlation in images has shown great potential in deep learning networks, such as the self-attention mechanism in the channel domain and the self-similarity mechanism in the spatial domain.
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation
This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain.
Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution
A na\"ive method is to decompose it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR).
Space-time Video Super-resolution
Video Frame Interpolation
+1
Recent Advances on Neural Network Pruning at Initialization
Neural network pruning typically removes connections or neurons from a pretrained converged model; while a new pruning paradigm, pruning at initialization (PaI), attempts to prune a randomly initialized network.
GAN Inversion: A Survey
GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generator.
Context Reasoning Attention Network for Image Super-Resolution
However, the basic convolutional layer in CNNs is designed to extract local patterns, lacking the ability to model global context.
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution
Specifically, we propose a dynamic high-pass filtering (HPF) module that locally applies adaptive filter weights for each spatial location and channel group to preserve high-frequency signals.
Neural Pruning via Growing Regularization
Regularization has long been utilized to learn sparsity in deep neural network pruning.
Neural Sparse Representation for Image Restoration
Inspired by the robustness and efficiency of sparse representation in sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks.
Pyramid Attention Networks for Image Restoration
Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales.
Adversarial Feature Hallucination Networks for Few-Shot Learning
The recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data.
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
Rather than synthesizing missing LR video frames as VFI networks do, we firstly temporally interpolate LR frame features in missing LR video frames capturing local temporal contexts by the proposed feature temporal interpolation network.
Ranked #4 on
Video Frame Interpolation
on Vid4 - 4x upscaling
Space-time Video Super-resolution
Video Frame Interpolation
+1
Texture Hallucination for Large-Factor Painting Super-Resolution
We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e. g., 8X, 16X).
Joint Super-Resolution and Alignment of Tiny Faces
As for SR, the proposed method recovers sharper edges and more details from LR face images than other state-of-the-art methods, which we demonstrate qualitatively and quantitatively.
Visual Semantic Reasoning for Image-Text Matching
It outperforms the current best method by 6. 8% relatively for image retrieval and 4. 8% relatively for caption retrieval on MS-COCO (Recall@1 using 1K test set).
Ranked #8 on
Image Retrieval
on Flickr30K 1K test
FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
Most current image super-resolution (SR) methods based on convolutional neural networks (CNNs) use residual learning in network structural design, which favors to effective back propagation and hence improves SR performance by increasing model scale.
Multimodal Style Transfer via Graph Cuts
An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices.
Residual Non-local Attention Networks for Image Restoration
To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.
Residual Dense Network for Image Restoration
We fully exploit the hierarchical features from all the convolutional layers.
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames).
Channel Splitting Network for Single MR Image Super-Resolution
The proposed CSN model divides the hierarchical features into two branches, i. e., residual branch and dense branch, with different information transmissions.
Ranked #3 on
Image Super-Resolution
on IXI
Support Neighbor Loss for Person Re-Identification
To ensure scalability and separability, a softmax-like function is formulated to push apart the positive and negative support sets.
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
To solve these problems, we propose the very deep residual channel attention networks (RCAN).
Ranked #15 on
Image Super-Resolution
on BSD100 - 4x upscaling
Residual Dense Network for Image Super-Resolution
In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers.
Ranked #3 on
Color Image Denoising
on CBSD68 sigma50
