On Demand Learning Save

On-Demand Learning for Deep Image Restoration

[Project Page] [arXiv]

This repository contains the training code for our ICCV 2017 paper on All-Rounders for Image Restoration. We propose an on-demand learning algorithm for training image restoration models with deep convolutional neural networks that can generalize across difficulty levels. This repo contains our code for four image restoration tasks---image inpainting, pixel interpolation, image deblurring, and image denoising. The code is adapted from Soumith's DCGAN Implementation and Deepak Pathak's Context Encoder Implementation.

If you find our code or project useful in your research, please cite:

    @inproceedings{gao2017on-demand,
      title={On-Demand Learning for Deep Image Restoration},
      author={Gao, Ruohan and Grauman, Kristen},
      booktitle={ICCV},
      year={2017}
    }

Please also consider citing:

    @inproceedings{radford2016unsupervised,
      title={Unsupervised representation learning with deep convolutional generative adversarial networks},
      author={Radford, Alec and Metz, Luke and Chintala, Soumith},
      booktitle={ICLR},
      year={2016}
    }
    
    @inproceedings{pathak2016context,
      title={Context Encoders: Feature Learning by Inpainting},
      author={Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A},
       booktitle={CVPR},
      year={2016}
    }

Contents

0. Preparation
1. Image Inpainting
2. Pixel Interpolation
3. Image Deblurring
4. Image Denoising
5. Image Denoising of Arbitrary Sizes
6. Sample Code for All Training Schemes

1) Preparation

1. Install Torch: http://torch.ch/docs/getting-started.html

2. Install torch-opencv: https://github.com/VisionLabs/torch-opencv/wiki/Installation

3. Clone the repository

git clone https://github.com/rhgao/on-demand-learning.git

4. Download CelebA or SUN397 dataset, or prepare your own dataset.

# put all training images inside my_train_set/images/
mkdir -p /your_path/my_train_set/images/
# put all validation images inside my_val_set/images/
mkdir -p /your_path/my_val_set/images/
# put all testing images inside my_test_set/images/
mkdir -p /your_path/my_test_test/images/
cd on-demand-learning/
ln -sf /your_path dataset

5. [Optional] If you want to run a quick demo for the four image restoration tasks, please download our pre-trained models using the following script.

cd models
#download image inpainting model
bash download_model.sh inpainting
#download pixel interpolation model
bash download_model.sh pixelInterpolation
#download image deblurring model
bash download_model.sh deblurring
#download image denoising model
bash download_model.sh denoising
#download our image denoising model equipped to denoise images of any size
bash download_model.sh denoise_anysize

6. [Optional] Install the Display Package, which enables you to track the training progress. If you don't want to install it, please set display=0 in train.lua.

luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec
#start the display server
th -ldisplay.start 8000
# on client side, open in browser: http://localhost:8000/
# You can then see the training progress in your browser window.

2) Image Inpainting

1. Demo

# Test the image inpainting model on various corruption levels
cd inpainting
DATA_ROOT=../dataset/my_test_set name=inpaint_demo net=../models/inpainting_net_G.t7 manualSeed=333 gpu=1 display=1 th demo.lua
# Demo results saved as inpaint_demo.png

2. Train the model

# Train your own image inpainting model
cd inpainting
DATA_ROOT=../dataset/my_train_set name=inpaint niter=250 loadSize=96 fineSize=64 display=1 display_iter=50 gpu=1 th train.lua

3) Pixel Interpolation

1. Demo

# Test the pixel interpolation model on various corruption levels
cd pixelInterpolation
DATA_ROOT=../dataset/my_test_set name=pixelInter_demo net=../models/pixelInterpolation_net_G.t7 manualSeed=333 gpu=1 display=1 th demo.lua
# Demo results saved as pixelInter_demo.png

2. Train the model

# Train your own pixel interpolation model
cd pixelInterpolation
DATA_ROOT=../dataset/my_train_set name=pixel niter=250 loadSize=96 fineSize=64 display=1 display_iter=50 gpu=1 th train.lua

4) Image Deblurring

1. Demo

# Test the image deblurring model on various corruption levels
cd deblurring
DATA_ROOT=../dataset/my_test_set name=deblur_demo net=../models/deblurring_net_G.t7 manualSeed=333 gpu=1 display=1 th demo.lua
# Demo results saved as deblur_demo.png

2. Train the model

# Train your own image deblurring model
cd deblurring
DATA_ROOT=../dataset/my_train_set name=deblur niter=250 loadSize=96 fineSize=64 display=1 display_iter=50 gpu=1 th train.lua

5) Image Denoising

1. Demo

# Test the image denoising model on various corruption levels
cd deblurring
DATA_ROOT=../dataset/my_test_set name=denoise_demo net=../models/denoising_net_G.t7 sigma=25 manualSeed=333 gpu=1 display=1 th demo.lua
# Demo results saved as denoise_demo.png

2. Train the model

# Train your own image denoising model
cd denoising
DATA_ROOT=../dataset/my_train_set name=denoise niter=1500 loadSize=96 fineSize=64 display=1 display_iter=50 gpu=1 th train.lua

6) Image Denoising of Arbitrary Sizes

Denoising/DB11 contains 11 classic images commonly used to evaluate image denoising algorithms. Because the input of our network is of size 64 x 64, given an image of arbitrary size (assuming larger than 64 x 64), we use a sliding-window approach to denoise each patch separately, then average outputs at overlapping pixels.

# Denoise classic image Lena from DB11 dataset
cd denoise_anysize
img_path=DB11/Lena.png name=denoise net=../models/denoise_anysize_net_G.t7 sigma=25 stepSize=3 gpu=1 th denoise.lua
# Denoising results saved as denoise.png

7) Sample Code for Training Schemes

training_schemes contains a sample script that can be adapted for different training schemes attempted in the paper, including on-demand learning, rigid-joint learning, staged (anti-)curriculum learning and cumulative (anti-)curriculum learning.