Tag Archives: matrix

Opening Raspberry Pi High Quality Camera Raw Files

The Raspberry Pi Foundation recently released an interchangeable lens camera module based on the Sony  IMX477, a 1/2.3″ back side illuminated sensor with 3040×4056 pixels of 1.55um pitch.  In this somewhat technical article we will unpack the 12-bit raw still data that it produces and render it in a convenient color space.

still life raw capture data file raspberry pi high quality hq cam f/8 1/2s base analog gain iso adobe rgb
Figure 1. 12-bit raw capture by Raspberry Pi High Quality Camera with 16 mm kit lens at f/8, 1/2 s, base ISO. The image was loaded into Matlab and rendered Half Height Nearest Neighbor in the Adobe RGB color space with a touch of local contrast and sharpening.  Click on it to see it in its own tab and view it at 100% magnification. If your browser is not color managed you may not see colors properly.

Continue reading Opening Raspberry Pi High Quality Camera Raw Files

The Perfect Color Filter Array

We’ve seen how humans perceive color in daylight as a result of three types of photoreceptors in the retina called cones that absorb wavelengths of light from the scene with different sensitivities to the arriving spectrum.

Figure 1.  Quantitative Color Science.

A photographic digital imager attempts to mimic the workings of cones in the retina by usually having different color filters arranged in an array (CFA) on top of its photoreceptors, which we normally call pixels.  In a Bayer CFA configuration there are three filters named for the predominant wavelengths that each lets through (red, green and blue) arranged in quartets such as shown below:

Figure 2.  Bayer Color Filter Array: RGGB  layout.  Image under license from Cburnett, pixels shifted and text added.

A CFA is just one way to copy the action of cones:  Foveon for instance lets the sensing material itself perform the spectral separation.  It is the quality of the combined spectral filtering part of the imaging system (lenses, UV/IR, CFA, sensing material etc.) that determines how accurately a digital camera is able to capture color information from the scene.  So what are the characteristics of better systems and can perfection be achieved?  In this article I will pick up the discussion where it was last left off and, ignoring noise for now, attempt to answer this  question using CIE conventions, in the process gaining insight in the role of the compromise color matrix and developing a method to visualize its effects.[1]  Continue reading The Perfect Color Filter Array

Bayer CFA Effect on Sharpness

In this article we shall find that the effect of a Bayer CFA on the spatial frequencies and hence the ‘sharpness’ information captured by a sensor compared to those from the corresponding monochrome version can go from (almost) nothing to halving the potentially unaliased range – based on the chrominance content of the image and the direction in which the spatial frequencies are being stressed. Continue reading Bayer CFA Effect on Sharpness

Linear Color: Applying the Forward Matrix

Now that we know how to create a 3×3 linear matrix to convert white balanced and demosaiced raw data into XYZ_{D50}  connection space – and where to obtain the 3×3 linear matrix to then convert it to a standard output color space like sRGB – we can take a closer look at the matrices and apply them to a real world capture chosen for its wide range of chromaticities.

Figure 1. Image with color converted using the forward linear matrix discussed in the article.

Continue reading Linear Color: Applying the Forward Matrix

Color: Determining a Forward Matrix for Your Camera

We understand from the previous article that rendering color with Adobe DNG raw conversion essentially means mapping raw data in the form of rgb triplets into a standard color space via a Profile Connection Space in a two step process

    \[ Raw Data \rightarrow  XYZ_{D50} \rightarrow RGB_{standard} \]

The first step white balances and demosaics the raw data, which at that stage we will refer to as rgb, followed by converting it to XYZ_{D50} Profile Connection Space through linear projection by an unknown ‘Forward Matrix’ (as DNG calls it) of the form

(1)   \begin{equation*} \left[ \begin{array}{c} X_{D50} \\ Y_{D50} \\ Z_{D50} \end{array} \right] = \begin{bmatrix} a_{11} & a_{12} & a_{13} \\ a_{21} & a_{22} & a_{23} \\ a_{31} & a_{32} & a_{33} \end{bmatrix} \left[ \begin{array}{c} r \\ g \\ b \end{array} \right] \end{equation*}

with data as column-vectors in a 3xN array.  Determining the nine a coefficients of this matrix M is the main subject of this article[1]. Continue reading Color: Determining a Forward Matrix for Your Camera

Color: From Object to Eye

How do we translate captured image information into a stimulus that will produce the appropriate perception of color?  It’s actually not that complicated[1].

Recall from the introductory article that a photon absorbed by a cone type (\rho, \gamma or \beta) in the fovea produces the same stimulus to the brain regardless of its wavelength[2].  Take the example of the eye of an observer which focuses  on the retina the image of a uniform object with a spectral photon distribution of 1000 photons/nm in the 400 to 720nm wavelength range and no photons outside of it.

Because the system is linear, cones in the foveola will weigh the incoming photons by their relative sensitivity (probability) functions and add the result up to produce a stimulus proportional to the area under the curves.  For instance a \gamma cone may see about 321,000 photons arrive and produce a relative stimulus of about 94,700, the weighted area under the curve:

equal-photons-per-wl
Figure 1. Light made up of 321k photons of broad spectrum and constant Spectral Photon Distribution between 400 and 720nm  is weighted by cone sensitivity to produce a relative stimulus equivalent to 94,700 photons, proportional to the area under the curve

Continue reading Color: From Object to Eye