Learning Sensor Multiplexing Design through Back-propagation
Python Shell
Switch branches/tags
Nothing to show
Clone or download
Latest commit e3584b5 Sep 29, 2016
Failed to load latest commit information.
data Public release. Sep 30, 2016
h5proc Public release. Sep 30, 2016
prototxt Public release. Sep 30, 2016
pylayers Public release. Sep 30, 2016
run Public release. Sep 30, 2016
.gitignore Public release. Sep 30, 2016
README.md Public release. Sep 30, 2016


Learning Sensor Multiplexing Design through Back-propagation

Copyright (C) 2016, Ayan Chakrabarti ayanc@ttic.edu

This distribution provides a framework for learning the color multiplexing pattern of a digital color camera, jointly with a neural network to interpolate and reconstruct full color images from the corresponding sensor measurements. It is a reference implementation of the algorithm described in the paper:

Ayan Chakrabarti, "Learning Sensor Multiplexing Design through Back-propagation," Advances in Neural Information Processing Systems (NIPS) 2016.

This implementation is being made available for non-commercial research use only. If you find this code useful in your research, please cite the above paper. See the project page for further details, and contact ayanc@ttic.edu with questions.

Table of Contents

  1. Requirements
  2. Preparing Data
  3. Pre-trained Models
  4. Reconstruction with Trained Models
  5. Training Sensor Patterns and Reconstruction Networks


We use caffe for training, using custom Python layers to simulate the input incident light and our learnable sensor layer. Therefore, if you want to train your own models (rather than just use the ones provided), you will need to compile caffe with support for Python layers. Moreover, you will need the skimage package for Python installed on your system. Make sure that it is installed with the freeimage plugin for reading and saving files. You will also need the command line HDF5 Tools to run the scripts that convert hdf5 caffe models to a python readable format.

We use a purely Python-based script, without any caffe dependencies, for reconstruction using a trained neural network model. These scripts require skimage to be installed. Moreover, if you want to these scripts to take advantage of a GPU, you will need to install the following python packages: pycuda and scikit-cuda.

Preparing Data

We use the Gehler-Shi dataset for training, validation, and testing. You will need to download the dataset and process them using the script convert.py provided in the data/ directory. To do so, download the image archives from the dataset page, and extract them into some location. Then, from within the data/ directory of this distribution, run:

$ cd data
$ ./convert.py /path/to/gehler_shi

where /path/to/gehler_shi is the directory where you extracted the database images. This will convert the original RAW images into normalized 8-bit PNG files, applying the required black-level correction to the Canon 5D images. The data/ directory already contains text files listing the split of this database into the training, validation, and test sets used in the experiments in the paper.

Pre-trained Models

You can download pre-trained reconstruction network models for different patterns and different noise levels as a .zip archive from the project page. These models are in Python's .npz format, with file names indicating the sensor pattern and noise level.

Specifically, bayer*.npz are for reconstructing from images sampled with the Bayer pattern, cfz*.npz for the sparse pattern of Chakrabarti et al. 2014, and lcfa*.npz for our learned pattern. This learned pattern corresponds to the following "code string" (see the next section for an explanation, and how to provide the code string to the reconstruction script):


The second part of each model's filename indicates the noise level for which it was trained. *0.npz are trained for noiseless observations, *25.npz for AWGN with standard deviation 0.0025, *50.npz for std. 0.005, and so on. These standard deviations are with respect to the input color image intensities in the range [0,1].

Reconstruction with Trained Models

The script run/runDM.py can be used to evaluate different CFA patterns and reconstruction networks. You can use these scripts with the provided pre-trained models, or for the models that you train yourself. Call the script with -h to get the full list of options. We given an overview of its two modes of operation below.

Simulate Sampling and Reconstruction: This is the standard mode of operation. Call the script on a set of full color RGB images: for each image, the script will simulate sampling with a specified sampling pattern with noise at a specified standard deviation, followed by reconstruction with the provided model. The script supports sampling with the Bayer pattern, [Chakrabarti et al. 2014]'s pattern, and an arbitrary pattern specified by a code string-to handle learned patterns. By default, the script only computes PSNR statistics in this setting, but you can specify an option to save the reconstructed images as well (as 16-bit PNGs).

Arbitrary patterns are specified by setting the environment variable LCFA_CODE to a code string, which is a sequence of 64 digits, each between 0 and 3, that specify which color is sampled at each pixel in the 8x8 pattern (serialized in reading order), with 0 indicating Red, 1 Green, 2 Blue, and 3 White.

For example, to compute reconstruction PSNR statistics on the learned sampling pattern with the provided pre-trained models, at noise std. 0.005, call the script as follows:

$ export LCFA_CODE=3133033303333303132232330233131233333103303133100323133331333322
$ cd data/
$ ../run/runDM.py --cfa lcfa --nstd 0.005 --wts /path/to/models/lcfa50.npz  `cat test.txt`

Reconstruct Already Sampled Images: You can also call the script on already sampled single channel images, in which case it will apply the reconstruction network on them directly. In this setting, the script outputs the reconstructed image, but does not compute PSNR stats (since it doesn't have access to the ground truth). To generate your own sampled images:

  • Please ensure that you use the same pattern layout convention as our code (see data/sensor.py or pylayers/sensor.py for reference). In particular, in Bayer-sampled images, the top left (x=0,y=0) pixel should be green, with (x=1,y=1) also green, (x=0,y=1) red, and (x=1,y=0) blue.

  • Since white intensities are assumed to be the sum of red, green, and blue, to ensure that all sampled intensities lie in the range [0,1], we divide the original RGB intensities by a factor of 3. The noise standard deviation is with respect to this scaling of the intensities, and the reconstruction network models are trained to go from this input and output RGB values at their original scale.

    You should follow the same intensity-scaling procedure if you generate your own sampled images. The only exception is for the Bayer pattern: as a convenience (and to enable comparisons to other Bayer demosaicking algorithms), the script assumes that intensities in provided Bayer-sampled images have NOT been divided by 3. Therefore, the script will scale down the intenities in the input images before passing them to the reconstruction network.

We recommend you read through the source code for a clearer understanding of these conventions.

Training Sensor Patterns and Reconstruction Networks

All training is done using caffe. The directory prototxt/ contains network definition proto-texts for different kinds of training: (1) training the sensor pattern jointly with a reconstruction network, and (2) training a reconstruction network for a fixed sensor pattern. The latter case is used both for the pattern we learn from (1), as well as for training competing reconstruction networks for traditional sensing patterns.

Custom Layers

These networks use various custom layers defined in python, and provided in the pylayers/ directory. You will need to ensure that this directory, along with the caffe/python directory, are included in your PYTHONPATH environment variable before calling the caffe executable. We provide a brief description of these layers below, and you can look at the proto-text files to see them in action.

Data Layer: The dcDataLayer.py file provides the DataRGBW layer. This layer loads 24x24 image patches cropped from images in a dataset (like the Gehler-Shi dataset that we use), and produces two "tops" for each patch: (1) a 4-channel blob (corresponding to noisy red, green, blue and white intensity measurements) which will be sub-sampled by a sensor layer, and (2) the ground truth RGB intensities of the center 8x8 patch that the network must learn to reconstruct.

The layer takes a parameter string (see proto-text files) in the following format: list.txt:batch_size:chunk_size:chunk_repeat:noise_std

Here, list.txt is the (full path to) a text file containing the list of images to load patches from: you can use the train.txt and val.txt files in the data directory of this distribution for training and validation, to recreate our experimental setup. You will also need to set the environment variable DC_DATA_DIR to the full path of the directory that contains these image files.

noise_std is simply the standard deviation of the AWGN noise to be added to the measurements of the first blob. batch_size is the number of patches to include in each batch, while chunk_size and chunk_repeat define the patch-sampling behavior. Specifically, the layer will load a set of chunk_size images every chunk_repeat iterations, and in each iteration, sample patches randomly from this set of images. This ensures that we minimize disk access and use multiple patches from the same image, but that every batch is diverse and has samples from multiple images.

Of course, for validation, you don't want this sampling to be random, since you want to be able to compare loss on the same patches every time you validate. If chunk_size is set to 0 (as it is in the TEST phase of your proto-text), then the data layer will go through the list of images one image at a time, using each image to generate batches for chunk_repeat iterations.

Sensor Layers: The sensor.py file provides four layer classes, each of which take in the channel 4-channel blob produced by the data layer, and output a single channel blob formed by sampling a specific channel at each location. Each class uses a different sampling pattern: Bayer samples according to the Bayer pattern, CFZ14 according to [Chakrabarti et al. 2014]'s pattern, and FLCFA according to an arbitrary pattern specified as a code string in its parameter string. The fourth kind of sensor layer is LCFA that actually learns the sensor pattern. A soft encoding of the pattern (see the paper for details) is a learnable parameter blob for this layer, and will be updated by caffe using SGD, and stored with the reconstruction weights in saved caffemodel files.

Training Procedure

The network architectures for various training settings are provided in the files in the prototxt/ directory. Note that these are only the network proto-texts. You will need to create a solver.prototxt file that: (a) saves weights in the HDF5 format (instead of protobuf), (b) trains using SGD with a momentum of 0.9, and (c) follows a learning rate schedule from the paper (and also detailed below). If you want to test on the validation set, you should set test_iter to the number of validation images (51 in our split defined in data/val.txt).

In the experiments in the paper, we first learned the sensor pattern at a noise-level of 0.01. The file learncfa.prototxt provides the network architecture to achieve this (if you want to learn the pattern at a different noise-level, you will have to modify the last parameter in the param_str for the data layers). To recreate the setting in the paper, train this network for 1.5e6 iterations with a learning rate of 0.001. Once you are done training, call the script h5proc/getCode.sh with a path to the trained caffemodel.h5 file, to retrieve the sensor code string.

We then train reconstruction networks at several different noise levels using this code, and the FLCFA sensor layer, passing the code string retrieved in the previous step in the param_str of that layer. The file flcfa50.prototxt provides the description of the network architecture for this (for a noise level of 0.005). These networks are trained for 1.5e6 iterations with a learning rate of 0.001, and another 1e5 iterations with lr=0.0001.

The bayer50.prototxt and cfz50.prototxt files can be used for training reconstruction networks for the Bayer and [Chakrabarti et al. 2014] pattern respectively. These should be trained with the same schedule as the flcfa50.prototxt network: 1.5e6 iterations with lr=0.001, followed by 1e5 iterations with lr=0.0001.

After you are done training the reconstruction networks, you can use the h5proc/h5npz.py script, e.g.:

h5npz.py /path/to/wts/bayer25_iter_1500000.caffemodel.h5 bayer25.npz

to convert the stored caffe model into the .npz format used by our reconstruction script run/runDM.py. Note that the architecture of the network is hard-coded into the reconstruction script, so if you choose to use a different reconstruction architecture, you'll have to make corresponding changes in the runDM.py script as well.