147 skills found · Page 1 of 5
xinntao / ESRGANECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
sanghyun-son / EDSR PyTorchPyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)
DachunKai / EvTexture[ICML 2024 & TPAMI 2026] EvTexture & EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution
ckkelvinchan / BasicVSR PlusPlusOfficial repository of "BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment"
swz30 / MIRNet[ECCV 2020] Learning Enriched Features for Real Image Restoration and Enhancement. SOTA results for image denoising, super-resolution, and image enhancement.
limbee / NTIRE2017Torch implementation of "Enhanced Deep Residual Networks for Single Image Super-Resolution"
swz30 / MIRNetv2[TPAMI 2022] Learning Enriched Features for Fast Image Restoration and Enhancement. Results on Defocus Deblurring, Denoising, Super-resolution, and image enhancement
DachunKai / Ev DeblurVSR[AAAI 2025] Event-Enhanced Blurry Video Super-Resolution
jmiller656 / EDSR TensorflowTensorflow implementation of Enhanced Deep Residual Networks for Single Image Super-Resolution
Saafke / EDSR TensorflowTensorFlow implementation of 'Enhanced Deep Residual Networks for Single Image Super-Resolution'.
kai422 / IART[CVPR 2024 Highlight] Enhancing Video Super-Resolution via Implicit Resampling-based Alignment.
hellloxiaotian / LESRCNNLightweight Image Super-Resolution with Enhanced CNN (Knowledge-Based Systems,2020)
atomicoo / EnhanceIMGImage-enhancement algorithms: low-light enhancement, image restoration, super-resolution reconstruction. 图像增强算法探索：低光增强、图像修复、超分辨率重建 ……
jlygit / AI Video EnhanceThis repository collects the state-of-the-art algorithms for video/image enhancement using deep learning (AI) in recent years, including super resolution, compression artifact reduction, deblocking, denoising, image/color enhancement, HDR.
USTCPCS / CVPR2018 AttentionContext Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark
claudiom4sir / StableVSR[ECCV 2024] Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion Models
leverxgroup / EsrganEnhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution
akshaydudhane16 / BIPNet[CVPR 2022--Oral, Best paper Finalist] Burst Image Restoration and Enhancement. SOTA for Burst Super-resolution, Low-light Burst Image Enhancement, Burst Image De-noising
liuzhen03 / Awesome Video EnhancementPaper list for video enhancement, including video super-resolution, interpolation, denoising, deblurring and inpainting.
peylnog / Unveiling Hidden Details A RAW Data Enhanced RealSROfficial Code for “Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution”
