Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise

Preview

There are 4 python files in this repo. The test data is under the /data/ folder.

• train_BaggedDIP_speckle.py: training Bagged-DIP based PGD algorithm.

• function_grad.py: i) explicit gradient function of the MLE loss function, and ii) implementation of Newton Schulz algorithm for efficently approximating matrix inverse.

• decoder.py: basic network structures of the Deep Image Prior/Deep Decoder we use in Bagged-DIP.

• utils.py: all the other helper functions.

Running the code

Run the Bagged-DIP PGD algorithm for recovering images:

python train_BaggedDIP_speckle.py

Specify the hyperparameters and experiment setting:

E.g., recover images from L=100, m/n=0.5 down-sampled complex-valued measurements:

python train_BaggedDIP_speckle.py --compression_rate 0.5 --patch_size 128 --lamb 1.0 --crop True --lr_NN 1e-3 --lr_GD 0.01 --outer_ite 100 --num_look 100 --weight_decay 0.0 --test_name 'Set11' --DIP_avg True --use_complex True

Relevant works on speckle noise

[1] Chen, Xi, Christopher Metzler, Arian Maleki, and Shirin Jalali. "Novel approach to coherent imaging in the presence of speckle noise." Unconventional Imaging, Sensing, and Adaptive Optics 2024. Vol. 13149. SPIE, 2024. paper

[2] Chen, Xi, Zhewen Hou, Christopher Metzler, Arian Maleki, and Shirin Jalali. "Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise." Forty-first International Conference on Machine Learning (ICML 2024). paper

[3] Chen, Xi, Zhewen Hou, Christopher Metzler, Arian Maleki, and Shirin Jalali. "Multilook compressive sensing in the presence of speckle noise." In NeurIPS 2023 Workshop on Deep Learning and Inverse Problems. 2023. paper

[4] Zhou, Wenda, Shirin Jalali, and Arian Maleki. "Compressed sensing in the presence of speckle noise." IEEE Transactions on Information Theory 68.10 (2022): 6964-6980. paper