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Learning Memory Augmented Cascading Network for Compressed Sensing of Images

@inproceedings{chen2020learning,
  title={Learning memory augmented cascading network for compressed sensing of images},
  author={Chen, Jiwei and Sun, Yubao and Liu, Qingshan and Huang, Rui},
  booktitle={European Conference on Computer Vision},
  pages={513--529},
  year={2020},
  organization={Springer}
}

DATA SET

We utilize 91 images which are the common usage in CS training. We crop these images into the size of 96*96. We test our model in three dataset: SET11, BSD68 and MICCAI 2013(MRI).

TRAIN

just

$ python MAC_Net_train.py

TEST

$ python MAC_Net_test.py

Other things

Other details will be added.

New

If you want to simplify the generation of y from the image, you can modify the code:

Firstly, reshape the measurement matrix:

phi = np.reshape(A, (block_size, block_size, 1, size_y))

Secondly, use the function "tf.nn.conv2d" to realize the samling(measurement), for example:

y_meas = tf.nn.conv2d(t_target_image, A, (1, block_size, block_size, 1), padding='SAME')

This can be modified in "train.py" and "model.py" where use the "for" to generate the y. It is time-consuming to use the "for". So if you use the new code, it will be faster. Because it will be processed in GPU instead of CPU.

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Bug in test.py

Except for MR=0.25, the test code of SET11 produces the following error (e.g. for MR=0.1)

ValueError: Cannot reshape a tensor with 26 elements to shape [1,1,1,64] (64 elements) for 'cascade/Reshape_1' (op: 'Reshape') with input shapes: [1,26], [4] and with input tensors computed as partial shapes: input[1] = [1,1,1,64].