Code corresponding to the paper : (https://arxiv.org/abs/1612.04229)
Forked from the original code for RIDE, which can be found here
- Missing Pixel Interpolation
| Original Image | Masked Image | During Gradient Ascent | Recovered Image |
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- Single Pixel Camera Reconstruction
| Original Image | Initial Image | During Gradient Ascent | Recovered Image |
|---|---|---|---|
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Same as original RIDE code (https://github.com/lucastheis/ride/)
- Missing Pixel Interpolation
- Run
python experiments/map_interpolate_stack.pyfor default parameters. Following options can be changed :
-m/--model <Path to the model. Trained 1 layer and 2 layer models available in models/>
-d/--data <Path to the test images in mat format. Images chosen from BSDS dataset in the paper available in data/>
-h/--holes <Fraction of pixels removed from the image at random. Default is 70%>
-m/--momentum <Momentum set for gradient ascent in image reconstruction>
-l/--lr <Learning Rate set for gradient ascent in image reconstruction>
-N/--niter <Number of iterations for gradient ascent>
-p/--path <Path to save the resulting images>
-q/--mode <Mode to run Caffe in>
-D/-device <Device ID for GPU>
-s/--size <Size of test images>
-f/--flip <Flag to carry out direction flipping as mentioned in paper>
-e/--ent_max <For thresholding posterior entropy as mentioned in paper>
-r/-resume <For resuming the gradient ascent from previous npy file at certain iteration>
-I/--index <To select which test image to work on from the mat file>
- The test image will be divided into four parts and each gradient ascent will run on each part simultaneously. To stitch the four reconstructed parts use
python experiments/stitch_stack.py. Specify index of the test image using-Iand iteration to choose for the reconstructed npy file using-ioption
- Single Pixel Camera Reconstruction
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Create compressive sensing matrix using
python experiments/create_Phi.py -
Run
python experiments/map_single_pixel_stack.pyfor default parameters. Following options can be changed :
-m/--model <Path to the model. Trained 1 layer and 2 layer models available in models/>
-d/--data <Path to the test images in mat format. Images chosen from BSDS dataset in the paper available in data/>
-n/--noise_std <For adding noise to the sensed measurements. By default no noise is added>
-d/--momentum <Momentum set for gradient ascent in image reconstruction>
-l/--lr <Learning Rate set for gradient ascent in image reconstruction>
-N/--niter <Number of iterations for gradient ascent>
-p/--path <Path to save the resulting images>
-q/--mode <Mode to run Caffe in>
-D/-device <Device ID for GPU>
-s/--size <Size of test images>
-f/--flip <Flag to carry out direction flipping as mentioned in paper>
-e/--ent_max <For thresholding posterior entropy as mentioned in paper>
-r/-resume <For resuming the gradient ascent from previous npy file at certain iteration>
-K/--image_num <To select first K images from mat file to test >