嗨！thesis！哈尔滨工业大学毕业论文LaTeX模板

A collection of resources on personalized image generation.

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

[arXiv 2025] Official pytorch implementation of "FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors"

[arXiv 2024] PyTorch implementation of UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity

[CVPR2024] SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution

BokehMe: When Neural Rendering Meets Classical Rendering (CVPR 2022 Oral)

This framework includes the loading of different datasets as well as several denoising models. Several of the models are optimized for mobile usage. Those models include a simple U-Net, the winning…

[CVPR 2025] Complexity Experts are Task-Discriminative Learners for Any Image Restoration

[arXiv 2024] Deblur4DGS: 4D Gaussian Splatting from Blurry Monocular Video

Dense Prediction Transformers

[ECCV2024] "Raindrop Clarity: A Dual-Focused Dataset for Day and Night Raindrop Removal", https://arxiv.org/abs/2407.16957

FoundIR: Unleashing Million-scale Training Data to Advance Foundation Models for Image Restoration

Collection of recent nighttime enhancement works, including papers, codes, datasets, and metrics.

ACM MM'23 | The office repository of NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer

Adaptive Blind All-in-One Image Restoration

Learning Rain Location Prior for Nighttime Deraining (ICCV2023)

[ICCV 2023] Official repository for the paper "From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal".

[ACM MM Asia 2024 Oral] Official implementation of paper "LMHaze: Intensity-aware Image Dehazing with a Large-scale Multi-intensity Real Haze Dataset".

[ICCV 2023] Snow Removal in Video: A New Dataset and A Novel Method

Towards Ultra-High-Definition Image Deraining: A Benchmark and An Efficient Method

[ECCV2024 - Oral, Best Paper Award Candidate] SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow