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Found 19 papers, 8 papers with code
DMesh: A Differentiable Representation for General Meshes
We present a differentiable representation, DMesh, for general 3D triangular meshes.
Gradient Informed Proximal Policy Optimization
We introduce a novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization (PPO) algorithm.
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution
Single image super-resolution (SISR) is a challenging ill-posed problem that aims to up-sample a given low-resolution (LR) image to a high-resolution (HR) counterpart.
Content-Aware Local GAN for Photo-Realistic Super-Resolution
Recently, GAN has successfully contributed to making single-image super-resolution (SISR) methods produce more realistic images.
Differentiable Hybrid Traffic Simulation
To compute the gradient flow between two types of traffic models in a hybrid framework, we present a novel intermediate conversion component that bridges the lanes in a differentiable manner as well.
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image
The CVF module can output multiple decomposed variables of the input and take a combination of the outputs back as an input in a cyclic manner.
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network
Extensive studies demonstrate that our method outperforms the other self-supervised and even unpaired denoising methods by a large margin, without using any additional knowledge, e. g., noise level, regarding the underlying unknown noise.
C2N: Practical Generative Noise Modeling for Real-World Denoising
In a practical scenario, a noise generator should learn to simulate the general and complex noise distribution without using paired noisy and clean images.
Toward Real-World Super-Resolution via Adaptive Downsampling Models
Most image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs that are constructed by a predetermined operation, e. g., bicubic downsampling.
NTIRE 2021 Challenge on Video Super-Resolution
Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart.
NTIRE 2021 Challenge on Image Deblurring
In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions.
Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring
The supervised reblurring loss at training stage compares the amplified blur between the deblurred and the sharp images.
SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation
Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution (SR).
AIM 2020 Challenge on Video Temporal Super-Resolution
Videos in the real-world contain various dynamics and motions that may look unnaturally discontinuous in time when the recordedframe rate is low.
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning
Next, we formally introduce methods to extend parameter sharing to learning in heterogeneous observation and action spaces, and prove that these methods allow for convergence to optimal policies.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
NTIRE 2020 Challenge on Image and Video Deblurring
This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring.
AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results
Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low.
Clustering Convolutional Kernels to Compress Deep Neural Networks
In this paper, we propose a novel method to compress CNNs by reconstructing the network from a small set of spatial convolution kernels.
Enhanced Deep Residual Networks for Single Image Super-Resolution
Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN).
Ranked #1 on
Image Super-Resolution
on DIV2K val - 4x upscaling
(SSIM metric)
