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Figure 1: One possible developed version of the RAW image provided with the assignment.
1 Developing RAW images
For this problem, you will use the file campus.NEF included in the ./data directory of the assignment ZIP
archive. This is a RAW image that was captured with one of the course DSLR cameras (Nikon D3400). As
we discussed in class, RAW images do not look like standard images before first undergoing a “development”
process1
. The developed image should look something like the image in Figure 1. The final result can vary
1The term “develop” comes from the analogy with the process of developing film into a photograph. As discussed in class, we
often refer to this process as “rendering” the RAW images
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Homework assignment 1
15-463, 15-663, 15-862 Computational Photography, Fall 2023
Carnegie Mellon University
The purpose of this document is to introduce you to Python as a tool for
manipulating images. For this, you will build your own version of a very basic image processing pipeline
(without denoising). You will use this to turn the RAW image into an image that can be displayed on a
computer monitor or printed on paper. The Python packages required for this assignment are numpy, scipy,
skimage, and matplotlib.
Towards the end of this document, you will find a “Deliverables” section describing what you need to
submit. Throughout the writeup, we also mark in red questions you should answer in your submitted report.
Lastly, there is a “Hints and Information” section at the end of this document that is likely to help. We
strongly recommend that you read that section in full before you start to work on the assignment.
Figure 2: From left to right: ’grbg’, ’rggb’, ’bggr’, ’gbrg’.
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greatly, depending on the choices you make in your implementation of the image processing pipeline.
1.1 Implement a basic image processing pipeline
RAW image conversion. The RAW image file cannot be read directly by skimage. You will first need
to convert it into a .tiff file. You can do this conversion using a command-line tool called dcraw.
After you have downloaded and installed dcraw, you will first do a “reconnaissance run” to extract some
information about the RAW image. For this, call dcraw as follows:
dcraw -4 -d -v -w -T
In the output, you will see (among other information) the following:
Scaling with darkness , saturation , and
multipliers
Make sure to record the integer numbers for , , , , and , and
include them in your report.
Calling dcraw as above will produce a .tiff file. Do not use this! Instead, delete the file, and call dcraw
once more as follows (note the different flags):
dcraw -4 -D -T
This will produce a new .tiff file that you can use for the rest of this problem.
Python initials. We will be using skimage function imread for reading images. Originally, it will be in
the form of a numpy 2D-array of unsigned integers. Check and report how many bits per pixel the image
has, its width, and its height. Then, convert the image into a double-precision array. (See numpy functions
shape, dtype and astype.)
Linearization. The 2D-array is not yet a linear image. As we discussed in class, it is possible that it has
an offset due to dark noise, and saturated pixels due to over-exposure. Additionally, even though the
original data-type of the image was 16 bits, only 14 of those have meaningful information, meaning that
the maximum possible value for pixels is 4095 (that’s 2
12 − 1). For the provided image file, you can assume
the following: All pixels with a value lower than correspond to pixels that would be black, were it
not for noise. All pixels with a value above are saturated. The values for the black level
and for saturation are those you recorded earlier from the reconnaissance run of dcraw.
Convert the image into a linear array within the range [0, 1]. Do this by applying a linear transformation
(shift and scale) to the image, so that the value is mapped to 0, and the value is mapped
to 1. Then, clip negative values to 0, and values greater than 1 to 1. (See numpy function clip.)
Identifying the correct Bayer pattern. As we discussed in class, most cameras use the Bayer
pattern in order to capture color. The same is true for the camera used to capture our RAW image.
We do not know, however, the exact shift of the Bayer pattern. If you look at the top-left 2 × 2 square of
the image file, it can correspond to any of four possible red-green-blue patterns, as shown in Figure 2.

The 3×3 matrix MsRGB→cam transforms a 3×1 color vector from the sRGB color space to the camera-specific
RGB color space. We use its inverse to apply the transformation in the reverse way.
Computing the matrix MsRGB→cam involves a sequence of steps that will make a lot more sense once we
have gone through the color lecture later in the course. Here, we describe these steps at a high level, with
just enough detail for you to be able to implement them. We write the matrix MsRGB→cam as:
MsRGB→cam = MXYZ→cam · MsRGB→XYZ. (2)
As the notation suggests, the 3 × 3 matrix MsRGB→XYZ transforms a 3 × 1 color vector from the sRGB to
the XYZ color space; and analogously, the 3 × 3 matrix MXYZ→cam transforms a 3 × 1 color vector from the
XYZ to the camera-specific RGB color space. Their product, then, implements the transformation from the
sRGB to the camera-specific RGB color space, as we want. The XYZ color space is an intermediate reference
color space that color management systems use to accurately perform color space transformations.
The sRGB standard [1] provides the following values that you can use in your implementation:
MsRGB→XYZ =


0.4124564 0.3575761 0.1804375
0.2126729 0.7151522 0.0721750
0.0193339 0.1191920 0.9503041

 . (3)
You can find the values of the camera-specific matrix MXYZ→cam in the raw code of dcraw, under the
function adobe coeff. Look for the entry corresponding to the camera used to capture the RAW image you
are developing. Note that the dcraw code provides the matrix values multiplied by 10, 000, so make sure
to adjust the matrix you use in your implementation accordingly. Additionally, the dcraw code shows the
matrix as a 1 × 9 vector, which you should rearrange to a 3 × 3 matrix assuming row-major ordering.
After computing the matrix MsRGB→cam as in Equation (2), normalize it so that its rows sum to 1.
You need to do this so that you do not have to perform white balancing a second time. Finally, plug the
normalized matrix into Equation (1) to correct the color space of your image.
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Think of a way for identifying which Bayer pattern applies to your image file, and report the one you
identified. It will likely be easier to identify the correct Bayer pattern after performing white balancing.
White balancing. After identifying the correct Bayer pattern, we want to perform white balancing.
Implement both the white world and gray world white balancing algorithms, as discussed in class.
Additionally, implement a third white balancing algorithm, where you multiply the red, green, and blue
channels with the , , and values you recorded earlier from the reconnaissance
run of dcraw. These values are the white balancing presets the camera uses. After completing the entire
developing process, check what the image looks like when using each of the three white balancing algorithms,
decide which one you like best, and report your choice. (See numpy functions max and mean.)
Demosaicing. Once white balancing is done, it is time to demosaic the image. Use bilinear interpolation
for demosaicing, as discussed in class. Do not implement bilinear interpolation on your own!Instead, use
scipy’s built-in interp2d function.
Color space correction You now have an RGB image that is viewable with the standard matplotlib
display functions. However, the image color does not look quite right. This is because the image pixels have
RGB coordinates in the color space determined by the camera’s spectral sensitivity functions, and not in the
“standard” color space that image viewing software expects. We use scary quotes for “standard”because
what color space the image viewing software actually assumes, and what color space it should assume,are
neither the same nor trivial to determine. We will discuss these issues during the color lecture, but in the
meantime, a good default choice is to use the linear sRGB color space [1].
To transform your image to the linear sRGB color space, implement a function that applies the following
linear transformation to the RGB vector [Rcam, Gcam, Bcam]
⊤
of each pixel of your demosaiced image:
Cnon−linear =
(
12.92 · Clinear, Clinear ≤ 0.0031308,
(1 + 0.055) · C
1
2.4
linear − 0.055, Clinear > 0.0031308,
(4)
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Brightness adjustment and gamma encoding. You now have a 16-bit, full-resolution,linear RGB
image. Because of the scaling you did at the start of the assignment, the image pixel values may not be in a
range appropriate for display. Moreover, the image is not yet gamma-encoded. As we discussed in class, this
means that when you display the image, it will appear very dark.
Brighten the image by linearly scaling it. Select the scale to set the post-brightening mean grayscale
intensity to some value in the range [0, 1]. After the scaling, clip all intensity values greater than 1 to 1. (See
skimage function rgb2gray for converting the image to grayscale, and numpy function clip for clipping.)
What the best value is is a highly subjective judgment. You should experiment with many percentages and
report and use the one that looks best to you. For the photographically inclined, selecting a post-brightening
mean value of 0.25 is equivalent to scaling the image so that there are two stops of bright area detail.
You are now one step away from having an image that can be properly displayed. The last thing you
need to take care of is tone reproduction (also known as gamma encoding). For this, implement the following
non-linear transformation, then apply it to the image:
where C = {R, G, B} is each of the red, green, and blue channels. This odd-looking function is not arbitrary:
it corresponds to the tone reproduction curve specified in the sRGB standard [1], which also specifies the
sRGB color space you used earlier. It is a good default choice if the camera’s true tone reproduction curve is
unknown. We will discuss sRGB during the color lecture, but you are welcome to read up on it on Wikipedia.
Compression (5 points). Finally, it is time to store the image, either with or without compression. Use
skimage function imsave to store the image in .PNG format (no compression), and also in .JPEG format with
the quality parameter set to 95. This setting determines the amount of compression. Can you tell the
difference between the two files? The compression ratio is the ratio between the size of the uncompressed file
(in bytes) and the size of the compressed file (in bytes). What is the compression ratio?
By changing the JPEG quality settings, determine the lowest setting for which the compressed image is
indistinguishable from the original. What is the compression ratio?
1.2 Perform manual white balancing
As we discussed in class, one way to do manual white balancing is by: 1) selecting some patch in the scene
that you expect to be white; and 2) normalizing all three channels using weights that make the red, green,
and blue channel values of this patch be equal. Implement this algorithm, and experiment with different
patches in the scene. Show the corresponding results, and discuss which patches work best. (See matplotlib
function ginput for interactively recording image coordinates).
1.3 Learn to use dcraw
Besides converting RAW files to .tiff, dcraw provides options to emulate all steps in the image processing
pipeline, including steps that are camera-dependent. Read through dcraw’s documentation, and figure
out what the correct flags are in order for dcraw to perform all the image processing pipeline steps you
implemented in Python. Make sure to report the exact dcraw flags you used.
Note that dcraw outputs images in .PPM format and does not have an option to use the .PNG format. You
will need to use an image editor to convert the image format. The command-line utility pnmtopng, available
in the Netpbm toolkit, provides an easy way to perform the conversion. Alternatively, ImageMagick also does
command-line format conversions.
You should now have (at least) two developed versions of the original RAW image: One (or more) you
produced using your implementation of the image processing pipeline, and another you produced using
dcraw. One more developed image is available as the file campus.jpg included in the ./data directory of
the assignment ZIP archive. This is the developed image produced by the camera’s own image processing
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pipeline, alongside the RAW image you have been using. Compare the three developed images, and explain
any differences between them. Which of the three images do you like best?
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2 Deliverables
When submitting your solution, make sure to follow the homework submission guidelines available on the
course website. Your submitted solution should include the following:
• A PDF report explaining what you did for each problem, including answers to all questions asked in
Problems 1 and 2 (marked in red throughout the writeup), as well as any of the bonus problems you
choose to do. The report should include any figures and intermediate results that you think may help.
Make sure to include explanations of any issues that you may have run into that prevented you from
fully solving the assignment, as this will help us determine partial credit. The report should also explain
any .PNG files you include in your solution (see below).
• All your Python code, including code for the bonus problems you choose to complete, as well as a
README file explaining how to use the code.
fig = plt.figure() # create a new figure
fig.add_subplot(1, 2, 1) # draw first image
plt.imshow(im1)
fig.add_subplot(1, 2, 2) # draw second image
plt.imshow(im2)
plt.savefig(’output.png’) # saves current figure as a PNG file
plt.show() # displays figure
Note that figures can also be saved by clicking on the save icon in the display window.
When displaying grayscale (single-channel) images, imshow maps pixel values in the [0, 1] range to colors
from a default color map. If you want to display your image in grayscale, you should use:
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• For Problem 1.1: At least two .PNG files, showing the final images you created with the two different
types of automatic white balancing. You can also include .PNG files for various experimental settings if
you want (e.g., different brightness values).
• For Problem 1.2: .PNG files, showing the results of manual white balancing for different patch selections.
• For Problem 1.3: The .PNG file produced by dcraw.
3 Hints and Information
dcraw version. Make sure to download and install the latest version (version 9.28) of dcraw. In particular,
the default version that comes in older Windows versions does not support the cameras used in this class,
and will produce results with a strong purple hue.
Package management. We recommend using Anaconda for Python package management. Once you install
conda, you can create an environment specifically for the class as follows:
conda create --name numpy scipy scikit-image
If you feel more comfortable using some other Python package management system, such as pip, you are
welcome to do so. Either way, make sure you have at least versions 1.19.1 for numpy, 1.5.0 for scipy, 0.16.2
for skimage, and 3.3.1 for matplotlib. Newer versions should be fine.
Image visualization. The package matplotlib is included with the skimage installation and is very useful
for visualizing intermediate results and creating figures for the report. For example, the following code reads
in two images and creates a single figure displaying them side by side:
import matplotlib.pyplot as plt
from skimage import io
# read two images from current directory
im1 = io.imread(‘image1.tiff’)
im2 = io.imread(‘image2.tiff’)
# display both images in a 1x2 grid
# read a single-channel image
imgr = io.imread(‘image_gray.tiff’)
plt.imshow(imgr), cmap=‘gray’)
plt.show()
You will need this in several places in this assignment, as many of your images will be grayscale.
Indexing. You can access subsets of numpy arrays using the standard Python slice syntax i:j:k, where i is
the start index, j is the end index, and k is the step size. The following example, given an original image im,
creates three other images, each with only one-fourth the pixels of the originals. The pixels of each of the
corresponding sub-images are shown in Figure 4. You can also use the numpy function dstack to combine
these three images into a single 3-channel RGB image.
import numpy as np
# create three sub-images of im as shown in figure below
im1 = im[0::2, 0::2]
im2 = im[0::2, 1::2]
im3 = im[1::2, 0::2]
# combine the above images into an RGB image, such that im1 is the red,
# im2 is the green, and im3 is the blue channel
im_rgb = np.dstack((im1, im2, im3));
Figure 4: Indexing sub-images.
Displaying results. You will find it very helpful to display intermediate results while you are implementing
the image processing pipeline. However, before you apply brightening and gamma encoding, you will find that
displayed images will look completely black. To be able to see something more meaningful, we recommend
scaling and clipping intermediate image im intermediate before displaying with matplotlib.
plt.imshow(np.clip(im_intermediate*5, 0, 1), cmap=‘gray’)
plt.show()
Figure 5 shows what you should see with this command after the linearization step.
Additionally, the colors of intermediate images will be very off (e.g., have a very strong green hue), even if
you are doing everything correctly. You will not get reasonable colors until after you have performed at least
white balancing. This will come into play when you are trying to determine the Bayer pattern.
What camera to use. If you decide to build your camera obscura using the camera provided by the course
(recommended), make sure to go over the camera tutorial available on the course website.
Exposure. In many point-and-shoot cameras, as well as in most manual-control cameras (DSLR, rangefinder,
mirrorless, and so on), exposures up to 30 seconds are readily available. If this exceeds your camera’s settings,
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Figure 5: Left: The linear RAW image (brightness increased by 5). Right: Crop showing the Bayer pattern.
you can reduce exposure times by using a large aperture size for your lens, as well as a large ISO setting
(possibly up to 1600).
Modern smartphone cameras typically have a hardware limit on maximum exposure time at around 4
seconds. However, many phones nowadays support a “long exposure” mode, where longer exposure times
are simulated by capturing a sequence of images and summing them all up at the RAW stage. This article
provides some information for both iOS and Android phones.
If you decide to use your smartphone, you should also use an app that enables manual control of camera
settings (especially focus, exposure time, and ISO). Additionally, make sure to disable the flash.
gphoto2. If you use the camera provided by the course, then we recommend that you tether it to your
laptop using the USB cable that came with the camera, so that you can control all its settings remotely. For
this, we recommend that you use gphoto2. For example, here is how to use gphoto2 from the command line
to take a photo with your camera:
gphoto2 --capture-image-and-download --force-overwrite --filename FILENAME.%C
Data capture tips. Below are some tips that will hopefully make capturing images with your pinhole
camera easier:
• We strongly recommend that you go outside during a sunny day. Capturing images indoors will require
extremely long exposures, which is impractical.
• Make sure to place your pinhole camera on a surface that is absolutely steady throughout the exposure
time. Definitely do not hole it with your hand.
• When focusing your digital camera, do so with the aperture of your lens fully open (even if you later
decide to stop down the aperture while capturing images.
• If your photographs are blurry, it could be because either your camera is not focused correctly on the
screen, or the pinhole is too large. To make sure that it is not a focusing issue, place a piece of paper
with text on the screen and take a photo (or look at the viewfinder of your digital camera) with your
box open. Make sure the camera is focused so that the text appears sharp. If the issue is the size of
your pinhole, try another one that is smaller.
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5 Credits
A lot of inspiration for the first part of this assignment came from Robert Sumner’s popular guide for
reading and processing RAW files. Some components of the first part were adapted from James Tompkin’s
computational photography course at Brown. Some inspiration for the second part came from James Hays’
and Gordon Wetzstein’s computational photography courses, at Brown and Stanford respectively.
References
[1] International Electrotechnical Commission and others. IEC 61966-2-1:1999. Multimedia systems and
equipment–Colour measurements and management–Part 2-1: Colour management–Default RGB colour
space–sRGB, 1999.
[2] A. Torralba and W. T. Freeman. Accidental pinhole and pinspeck cameras: Revealing the scene outside
the picture. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 374–381.
IEEE, 2012.
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