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antordragon's Projects

aamanddcm icon aamanddcm

This project is to implement “Attention-Adaptive and Deformable Convolutional Modules for Dynamic Scene Deblurring(with ERCNN)” . To run this project you need to setup the environment, download the dataset, and then you can train and test the network models. ## Prerequiste The project is tested on Ubuntu 16.04, GPU Titan XP. Note that one GPU is required to run the code. Otherwise, you have to modify code a little bit for using CPU. If using CPU for training, it may too slow. So I recommend you using GPU strong enough and about 12G RAM. ## Dependencies Python 3.5 or 3.6 are recommended. ``` tqdm==4.19.9 numpy==1.17.3 torch==1.0.0 Pillow==6.1.0 torchvision==0.2.2 ``` ## Environment I recommend using ```virtualenv``` for making an environment. If you using ```virtualenv```, ## Dataset I use GOPRO dataset for training and testing. __Download links__: [GOPRO_Large](https://drive.google.com/file/d/1H0PIXvJH4c40pk7ou6nAwoxuR4Qh_Sa2/view?usp=sharing) | Statistics | Training | Test | Total | | ----------- | -------- | ---- | ----- | | sequences | 22 | 11 | 33 | | image pairs | 2103 | 1111 | 3214 | After downloading dataset successfully, you need to put images in right folders. By default, you should have images on dataset/train and dataset/valid folders. ## Demo ## Training Run the following command ``` python demo_train.py ('data_dir' is needed before running ) ``` For training other models, you should uncommend lines in scripts/train.sh file. I used ADAM optimizer with a mini-batch size 16 for training. The learning rate is 1e-4. Total training takes 600 epochs to converge. To prevent our network from overfitting, several data augmentation techniques are involved. In terms of geometric transformations, patches are randomly rotated by 90, 180, and 270 degrees. To take image degradations into account, saturation in HSV colorspace is multiplied by a random number within [0.8, 1.2]. ![validation_curves](figs/validation_curve.png) ## Testing Run the following command ``` python demo_test.py ('data_dir' is needed before running ) ``` ## pretrained models if you need the pretrained models,please contact us by [email protected] ## Acknowledge Our code is based on Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring [MSCNN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Nah_Deep_Multi-Scale_Convolutional_CVPR_2017_paper.pdf), which is a nice work for dynamic scene deblurring .

aecr-net icon aecr-net

Contrastive Learning for Compact Single Image Dehazing, CVPR2021

epdn icon epdn

Enhanced Pix2pix Dehazing Network, accepted by CVPR 2019

fast-depth icon fast-depth

ICRA 2019 "FastDepth: Fast Monocular Depth Estimation on Embedded Systems"

ffa-net icon ffa-net

FFA-Net: Feature Fusion Attention Network for Single Image Dehazing

monoculardepth-inference icon monoculardepth-inference

Inference pipeline for the CVPR paper entitled "Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer" (http://www.atapour.co.uk/papers/CVPR2018.pdf).

mprnet icon mprnet

Official repository for "Multi-Stage Progressive Image Restoration" (CVPR 2021). SOTA results for image deblurring, deraining, and denoising.

msbdn-dff icon msbdn-dff

The source code of CVPR 2020 paper "Multi-Scale Boosted Dehazing Network with Dense Feature Fusion"

nbnet icon nbnet

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

srn-deblur icon srn-deblur

Repository for Scale-recurrent Network for Deep Image Deblurring

u-net icon u-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

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