LIDIA

Official pytorch implementation of the paper: "LIDIA: Lightweight Learned Image Denoising with Instance Adaptation"

Image denoising with adaptation

LIDIA is a lightweigh denoising network that can adapt itself to the input image, for example:



  clean astronomical image       noisy with σ = 50
      denoised, PSNR = 24.33dB   adaptation, PSNR = 26.25dB

Lightweight Image Denoiser

The Denoising Scheme

Our proposed method extracts all possible overlapping patches of size √n × √n from the processed image;
augment the patches with their nearest neighbors and cleans each patch in a similar way.
The final reconstructed image is obtained by combining these restored patches via averaging.

The proposed scheme includes a multi-scale treatment, fusing the processing of corresponding patches from different scales.

Results

Our network achieves near-SOTA results while using a very small number of parameters to be tuned.
Comparison between our algorithm and leading alternative ones by presenting their PSNR versus
their number of trained parameters shows that our networks, both LIDIA and LIDIA-S (LIDIA small),
achieve the best results among the lightweight networks.

      Comparing denoising networks: PSNR performance vs. the number

      of trained parameters (garyscale images, σ = 25)

Color denoising performance on BSD68 image set

Noise Level BM3D FFDNet DnCNN NLNet LIDIA (ours)
15 33.50 33.87 33.99 33.81 34.03
25 30.68 31.21 31.31 31.08 31.31
50 27.36 27.96 28.01 27.73 27.99
Average 30.51 31.01 31.10 30.87 31.11

Best PSNR marked in bold.

Some Qualitative Results







    clean       noisy with σ = 50       BM3D           DnCNN
          NLNet         LIDIA (ours)

                           
PSNR = 26.98dB   PSNR = 27.81dB   PSNR = 27.41dB   PSNR = 27.79dB

Instant Adaptation

We present a technique for updating the network for better treating the incoming image. This adaptation becomes highly effective
in cases of images deviating from the natural image statistics, or in situations in which the incoming image exhibits stronger inner-structure. In these cases,
denoising results are boosted dramatically, surpassing known supervised deep-denoisers.

External Adaptation Results

The external adaptation is useful when the input image deviates from the statistics of the training images.
In examples above the network was retrained on the image in the left column (marked as training image)

 


 


  training image         clean       noisy with σ = 50     DnCNN
      LIDIA (ours)       adaptation

                                         
  PSNR = 27.05dB   PSNR = 26.44dB   PSNR = 28.04dB

 


 


  training image         clean       noisy with σ = 50     DnCNN
      LIDIA (ours)       adaptation

                                         
  PSNR = 23.33dB   PSNR = 22.52dB   PSNR = 26.78dB

Internal Adaptation Results

In contrast to external adaptation, the internal one becomes effective when the incoming image is characterized by a high level of self-similarity.
For example, applying internal adaptation on images from Urban100 gains a notable improvement of almost 0.3dB in PSNR on average.

Internal adaptation for Urban100 color images (σ = 50)

DnCNN LIDIA (ours) LIDIA (ours) with adaptation
28.16 28.23 28.52

The internal adaptation can improve the denoising capability of the network, but the benefit varies significantly from image to another,
depending on its content.

      Histogram of improvement per image for Urban100 set (σ = 50)

Note that the internal adaptation procedure is not always successful. However, failures usually do not cause performance degradation,
indicating that this procedure, in the context of being deployed on a lightweight network, do not overfit.

Some Qualitative Results







      clean           noisy with σ = 50       DnCNN
          LIDIA (ours)         adaptation

                                   
PSNR = 28.64dB     PSNR = 28.77dB     PSNR = 29.28dB

Code

This code was tested with python 3.8, cuda 10.2 and pytorch 1.5.0 on GeForse GTX 1080 Ti.

Dependencies

  • numpy
  • matplotlib
  • imageio
  • Pillow
  • torch
  • torchvision

Install dependencies:

python -m pip install -r code/dependencies.txt

Color Image Denoising

Denoise a Color Image

python code/denoise_rgb.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda] [--plot] [--save]

Parameters

  • in_path - path to the test image. Default: images/BSD68/color/119082.png
  • out_path - path to the output image. Default: output/119082_s15_out.png
  • sigma - noise sigma: {15, 25, 50}. Default: 15
  • [--cuda] (optional) - use GPU
  • [--plot] (optional) - plot the processed image in a figure
  • [--save] (optional) - save the output image

For demo run

python code/denoise_rgb.py --cuda --plot

Denoise a Color Image Set

python code/denoise_set_rgb.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda] [--plot] [--save]

Parameters

  • in_path - path to the test set. Default: images/BSD68/color/
  • out_path - path to the output set. Default: output/
  • sigma - noise sigma: {15, 25, 50}. Default: 15
  • [--cuda] (optional) - use GPU
  • [--plot] (optional) - plot the processed images in a figure
  • [--save] (optional) - save the output images

For denoising color BSD68 set run

python code/denoise_set_rgb.py --cuda --save

For denoising color urban100 set run

python code/denoise_set_rgb.py --in_path images/urban100/color/ --save

Grayscale Image Denoising

Denoise a Grayscale Image

python code/denoise_bw.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda] [--plot] [--save]

Parameters

  • in_path - path to the test image. Default: images/BSD68/color/test011.png
  • out_path - path to the output image. Default: output/test011_s15_out.png
  • sigma - noise sigma: {15, 25, 50}. Default: 15
  • [--cuda] (optional) - use GPU
  • [--plot] (optional) - plot the processed image in a figure
  • [--save] (optional) - save the output image

For demo run

python code/denoise_bw.py --cuda --plot

Blind Denoising of a Grayscale Image

python code/blind_denoise_bw.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda] [--plot] [--save]

Parameters

  • in_path - path to the noisy image. Default: images/BSD68/color/test011.png
  • out_path - path to the output image. Default: output/test011_s15_out.png
  • sigma - noise sigma: {between 10 and 30}. Default: 15
  • [--cuda] (optional) - use GPU
  • [--plot] (optional) - plot the processed image in a figure
  • [--save] (optional) - save the output image

For demo run

python code/blind_denoise_bw.py --cuda --plot

Denoise a Grayscale Image with a Small Network

python code/denoise_bw_small.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda] [--plot] [--save]

Parameters

  • in_path - path to the test image. Default: images/BSD68/color/test011.png
  • out_path - path to the output image. Default: output/test011_s15_out.png
  • sigma - noise sigma: {15, 25, 50}. Default: 15
  • [--cuda] (optional) - use GPU
  • [--plot] (optional) - plot the processed image in a figure
  • [--save] (optional) - save the output image

For demo run

python code/denoise_bw_small.py --cuda --plot

Blind Denoising of a Grayscale Image with a Small Network

python code/blind_denoise_bw_small.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda] [--plot] [--save]

Parameters

  • in_path - path to the test image. Default: images/BSD68/color/test011.png
  • out_path - path to the output image. Default: output/test011_s15_out.png
  • sigma - noise sigma: {between 10 and 30}. Default: 15
  • [--cuda] (optional) - use GPU
  • [--plot] (optional) - plot the processed image in a figure
  • [--save] (optional) - save the output image

For demo run

python code/blind_denoise_bw_small.py --cuda --plot

Denoise a Grayscale Image Set

python code/denoise_set_bw.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda] [--plot] [--save]

Parameters

  • in_path - path to the test set. Default: images/BSD68/gray/
  • out_path - path to the output set. Default: output/
  • sigma - noise sigma: {15, 25, 50}. Default: 15
  • [--cuda] (optional) - use GPU
  • [--plot] (optional) - plot the processed images in a figure
  • [--save] (optional) - save the output images

For denoising garyscale BSD68 set run

python code/denoise_set_bw.py --cuda --save

For denoising garyscale urban100 set run

python code/denoise_set_bw.py --in_path images/urban100/gray/ --save

Bling Denoising of a Grayscale Image Set

python code/blind_denoise_set_bw.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda] [--plot] [--save]

Parameters

  • in_path - path to the test set. Default: images/BSD68/gray/
  • out_path - path to the output set. Default: output/
  • sigma - noise sigma: {between 10 and 30}. Default: 15
  • [--cuda] (optional) - use GPU
  • [--plot] (optional) - plot the processed images in a figure
  • [--save] (optional) - save the output images

For blind denoising BSD68 set run

python code/blind_denoise_set_bw.py --cuda --save

For blind denoising Urban100 set run

python code/blind_denoise_set_bw.py --in_path images/urban100/gray/ --save

Denoise a Grayscale Image Set with a Small Network

python code/denoise_set_bw_small.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda] [--plot] [--save]

Parameters

  • in_path - path to the test set. Default: images/BSD68/gray/
  • out_path - path to the output set. Default: output/
  • sigma - noise sigma: {15, 25, 50}. Default: 15
  • [--cuda] (optional) - use GPU
  • [--plot] (optional) - plot the processed images in a figure
  • [--save] (optional) - save the output images

For denoising BSD68 set with small network run

python code/denoise_set_bw_small.py --cuda --save

For denoising Urban100 set with small network run

python code/denoise_set_bw_small.py --in_path images/urban100/gray/ --save

Bling Denoising of a Grayscale Image Set with a Small Network

python code/blind_denoise_set_bw_small.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda] [--plot] [--save]

Parameters

  • in_path - path to the test set. Default: images/BSD68/gray/
  • out_path - path to the output set. Default: output/
  • sigma - noise sigma: {between 10 and 30}. Default: 15
  • [--cuda] (optional) - use GPU
  • [--plot] (optional) - plot the processed images in a figure
  • [--save] (optional) - save the output images

For blind denoising BSD68 set with small network run

python code/blind_denoise_set_bw_small.py --cuda --save

For blind denoising Urban100 set with small network run

python code/blind_denoise_set_bw_small.py --in_path images/urban100/gray/ --save

Internal Adaptation

Run Internal Adaptation Experiment on a Color Image

python code/internal_adaptation_rgb.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda_retrain] [--cuda_denoise] [--plot] [--save]

Parameters

  • in_path - path to the test image. Default: images/brick_house/color/test1_color.png
  • out_path - path to the output image. Default: output/
  • sigma - noise sigma: {15, 25, 50}. Default: 50
  • [--cuda_retrain] (optional) - use GPU in the retraining stage
  • [--cuda_denoise] (optional) - use GPU in the denoising stage
  • [--plot] (optional) - plot the processed image in a figure
  • [--save] (optional) - save the adapted network and output image

For demo run

python code/internal_adaptation_rgb.py --cuda_retrain --plot

Run Internal Adaptation Experiment on a Grayscale Image

python code/internal_adaptation_bw.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda_retrain] [--cuda_denoise] [--plot] [--save]

Parameters

  • in_path - path to the test image. Default: = images/brick_house/gray/test1_bw.png
  • out_path - path to the output image. Default: output/
  • sigma - noise sigma: {15, 25, 50}. Default: 50
  • [--cuda_retrain] (optional) - use GPU in the retraining stage
  • [--cuda_denoise] (optional) - use GPU in the denoising stage
  • [--plot] (optional) - plot the processed image in a figure
  • [--save] (optional) - save the adapted network and output image

For demo run

python code/internal_adaptation_bw.py --cuda_retrain --plot

Run Internal Adaptation Experiment on a Color Image Set

Runs internal adaptation experiment on each image in a set of color images

python code/internal_adaptation_set_rgb.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda_retrain] [--cuda_denoise] [--plot] [--save]

Parameters

  • in_path - path to the test set. Default: images/urban100/color/
  • out_path - path to the output set. Default: output/
  • sigma - noise sigma: {15, 25, 50}. Default: 50
  • [--cuda_retrain] (optional) - use GPU in the retraining stage
  • [--cuda_denoise] (optional) - use GPU in the denoising stage
  • [--plot] (optional) - plot the processed images in a figure
  • [--save] (optional) - save the adapted network and output images

For internal adaptation experiment on color Urban100 set run

python code/internal_adaptation_set_rgb.py --cuda_retrain --save

For internal adaptation experiment on color BSD68 set run

python code/internal_adaptation_set_rgb.py --in_path images/BSD68/color/ --cuda_retrain --cuda_denoise --save

Run Internal Adaptation Experiment on a Grayscale Image Set

Runs internal adaptation experiment on each image in a set of grayscale images

python code/internal_adaptation_set_bw.py --in_path <in_path> --out_path <out_path> --sigma <sigma> [--cuda_retrain] [--cuda_denoise] [--plot] [--save]

Parameters

  • in_path - path to the test set. Default: images/urban100/gray/
  • out_path - path to the output set. Default: output/
  • sigma - noise sigma: {15, 25, 50}. Default: 50
  • [--cuda_retrain] (optional) - use GPU in the retraining stage
  • [--cuda_denoise] (optional) - use GPU in the denoising stage
  • [--plot] (optional) - plot the processed images in a figure
  • [--save] (optional) - save the adapted network and output images

For internal adaptation experiment on grayscale Urban100 set run

python code/internal_adaptation_set_bw.py --cuda_retrain --save

For internal adaptation experiment on grayscale BSD68 set run

python code/internal_adaptation_set_bw.py --in_path images/BSD68/gray/ --cuda_retrain --cuda_denoise --save

External Adaptation

Run External Adaptation Experiment on a Color Image

python code/external_adaptation_rgb.py --in_path <in_path> --train_path <train_path> --out_path <out_path> --sigma <sigma> [--cuda_retrain] [--cuda_denoise] [--plot] [--save]
  • in_path - path to the test image. Default: images/astronomical/color/test/m36.png
  • train_path - path to the training image. Default: images/astronomical/color/test/m38.png
  • out_path - path to the output image. Default: output/
  • sigma - noise sigma: {15, 25, 50}. Default: 50
  • [--cuda_retrain] (optional) - use GPU in the retraining stage
  • [--cuda_denoise] (optional) - use GPU in the denoising stage
  • [--plot] (optional) - plot the processed image in a figure
  • [--save] (optional) - save the adapted network and output image

For demo run

python code/external_adaptation_rgb.py --cuda_retrain --plot

Run External Adaptation Experiment on a Grayscale Image

python code/external_adaptation_bw.py --in_path <in_path> --train_path <train_path> --out_path <out_path> --sigma <sigma> [--cuda_retrain] [--cuda_denoise] [--plot] [--save]
  • in_path - path to the test image. Default: images/astronomical/gray/test/m36.png
  • train_path - path to the training image. Default: images/astronomical/gray/test/m38.png
  • out_path - path to the output image. Default: output/
  • sigma - noise sigma: {15, 25, 50}. Default: 50
  • [--cuda_retrain] (optional) - use GPU in the retraining stage
  • [--cuda_denoise] (optional) - use GPU in the denoising stage
  • [--plot] (optional) - plot the processed image in a figure
  • [--save] (optional) - save the adapted network and output image

For demo run

python code/external_adaptation_bw.py --cuda_retrain --plot

Citation

If you use this code for your research, please cite our paper:

@InProceedings{Vaksman_2020_CVPR_Workshops,
  author = {Vaksman, Gregory and Elad, Michael and Milanfar, Peyman},
  title = {LIDIA: Lightweight Learned Image Denoising With Instance Adaptation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {June},
  year = {2020}
}

GitHub