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Image quality improvement with autoencoders for Deep Learning course.

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DL-IQIWA - Image quality improvement with autoencoders

In this project, we will work on an image quality improvement method. Given a set of high quality images, we will apply transformations that mimic the common quality issues. Then, we will train a network(autoencoder) to reconstruct the original, high quality image.

Link to wiki (notion)

TODO

  • Introduce different types of noises to cifar10 photos
  • Visualize dataset with different noises
  • Deliver synopsis
  • Build autoencoder
  • Build autoencoder
  • Denoise cifar10 photos
  • Cats and dogs dataset
  • Enhance Cats and dogs dataset photos resolution
  • Poster

For local development

Dev docker image: mactat/dl-iqiwa:latest

git clone <repo>
cd <project-dir>
docker compose pull
docker compose up

Go to http://127.0.0.1:42065/?token=pass

You can also set your jupyter interpreter in vscode to http://127.0.0.1:42065/?token=pass

Enjoy :)

For training without docker and jupyter

git clone <repo>

cd <project-dir>

Copy your kaggle key to /scrpts

cd scripts
chmod +x data data_extraction.sh 
./data data_extraction.sh  <name of kaggle key>
pip3 install -r requirements.txt
python3 train_model.py

Parameters of train_model.py:

python3 train_model.py --help
usage: train_model.py [-h] [--model MODEL] [--epochs EPOCHS] [--verbose VERBOSE]

Parameters for training

optional arguments:
  -h, --help         show this help message and exit
  --model MODEL      Specify the model file(without .py extension)
  --epochs EPOCHS    Specify the number of epochs
  --verbose VERBOSE  Print output or not

Storing, uplading and reusing models

Models will be stored in artifactory: https://dlmodels.jfrog.io To upload a model

curl -u<USERNAME>:<PASSWORD> -T <PATH_TO_FILE> "https://dlmodels.jfrog.io/artifactory/iqiwa-generic-local/<MODEL_NAME>.pth"

To download a model

curl https://dlmodels.jfrog.io/artifactory/iqiwa-generic-local/<MODEL_NAME>.pth > model.pth

Results from variational autoencoder

link to notebook

Model definition:

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================

            Conv2d-1          [128, 64, 16, 16]           3,136
            Conv2d-2           [128, 128, 8, 8]         131,200
            Linear-3                  [128, 10]          81,930
            Linear-4                  [128, 10]          81,930
           Encoder-5       [[-1, 10], [-1, 10]]               0
           
            Linear-6                [128, 8192]          90,112
   ConvTranspose2d-7          [128, 64, 16, 16]         131,136
   ConvTranspose2d-8           [128, 3, 32, 32]           3,075
           Decoder-9           [128, 3, 32, 32]               0
           
================================================================
Total params: 522,519
Trainable params: 522,519
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 1.50
Forward/backward pass size (MB): 54.02
Params size (MB): 1.99
Estimated Total Size (MB): 57.51
----------------------------------------------------------------
Number of parameters: 522519

Example of noised and denoised images:

Original image Reconstruction

Results from convolutional autoencoder:

link to notebook

Model definition:

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================

            Conv2d-1            [1, 32, 16, 16]           1,568
         MaxPool2d-2              [1, 32, 8, 8]               0
            Conv2d-3              [1, 16, 4, 4]           8,208
            Conv2d-4               [1, 8, 2, 2]           2,056
           Encoder-5               [1, 8, 2, 2]               0
           
   ConvTranspose2d-6              [1, 16, 4, 4]           2,064
          Upsample-7              [1, 16, 8, 8]               0
   ConvTranspose2d-8            [1, 32, 16, 16]           8,224
   ConvTranspose2d-9             [1, 3, 32, 32]           1,539
          Decoder-10             [1, 3, 32, 32]               0
          
================================================================
Total params: 23,659
Trainable params: 23,659
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 0.20
Params size (MB): 0.09
Estimated Total Size (MB): 0.30
----------------------------------------------------------------

Diagram

Example of image reconstruction

Original image Reconstruction Image with noise Reconstruction

Example of 400 noised and denoised images:

Results from enhanced convolutional autoencoder:

link to notebook

Model definition:

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================

            Conv2d-1            [1, 32, 16, 16]           1,568
         MaxPool2d-2              [1, 32, 8, 8]               0
            Conv2d-3              [1, 16, 4, 4]           8,208
            Conv2d-4               [1, 8, 2, 2]           2,056
           Encoder-5               [1, 8, 2, 2]               0
           
   ConvTranspose2d-6              [1, 16, 4, 4]           2,064
          Upsample-7              [1, 16, 8, 8]               0
   ConvTranspose2d-8            [1, 32, 16, 16]           8,224
   ConvTranspose2d-9             [1, 3, 32, 32]           1,539
          Decoder-10             [1, 3, 32, 32]               0
          
================================================================
Total params: 23,659
Trainable params: 23,659
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 0.20
Params size (MB): 0.09
Estimated Total Size (MB): 0.30
----------------------------------------------------------------

Example of image reconstruction

Original image Reconstruction Image with noise Reconstruction

Example of 400 noised and denoised images:

Results from enhancing images resolution using convolutional NN for cats and dogs:

link to script

Model definition:

Model definition:
----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
   ConvTranspose2d-1          [1, 10, 180, 180]             280
   ConvTranspose2d-2          [1, 20, 359, 359]           1,820
   ConvTranspose2d-3           [1, 3, 360, 360]             963
================================================================
Total params: 3,063
Trainable params: 3,063
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.37
Forward/backward pass size (MB): 25.10
Params size (MB): 0.01
Estimated Total Size (MB): 25.49
----------------------------------------------------------------

Diagram

Example of image reconstruction

Results from enhancing phots resolution using residual convolutional blocks:

link to script

Model definition:

Model definition: 
==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
Model                                    --                        --
├─UpsamplingBilinear2d: 1-1              [5, 3, 360, 360]          --
├─Conv2d: 1-2                            [5, 64, 360, 360]         1,792
├─ResidualBlock: 1-3                     [5, 64, 360, 360]         --
│    └─ConvolutionalBlock: 2-1           [5, 64, 360, 360]         --
│    │    └─Sequential: 3-1              [5, 64, 360, 360]         73,986
│    └─ConvolutionalBlock: 2-2           [5, 64, 360, 360]         --
│    │    └─Sequential: 3-2              [5, 64, 360, 360]         73,984
├─ResidualBlock: 1-4                     [5, 64, 360, 360]         --
│    └─ConvolutionalBlock: 2-3           [5, 64, 360, 360]         --
│    │    └─Sequential: 3-3              [5, 64, 360, 360]         73,986
│    └─ConvolutionalBlock: 2-4           [5, 64, 360, 360]         --
│    │    └─Sequential: 3-4              [5, 64, 360, 360]         73,984
├─Conv2d: 1-5                            [5, 3, 360, 360]          1,731
==========================================================================================
Total params: 299,463
Trainable params: 299,463
Non-trainable params: 0
Total mult-adds (G): 193.72
==========================================================================================
Input size (MB): 0.49
Forward/backward pass size (MB): 5655.74
Params size (MB): 1.20
Estimated Total Size (MB): 5657.43
==========================================================================================

Model artifacts

Example of image reconstruction

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