Tag Archives: linear algebra

Color, Perceptual, Sharpness

Cone Fundamentals & the LMS Color Space

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In the last article we showed how a digital camera’s captured raw data is related to Color Science.  In my next trick I will show that CIE 2012 2 deg XYZ Color Matching Functions \bar{x}, \bar{y}, \bar{z} displayed in Figure 1 are an exact linear transform of Stockman & Sharpe (2000) 2 deg Cone Fundamentals \bar{\rho}, \bar{\gamma}, \bar{\beta} displayed in Figure 2

(1)   \begin{equation*} \left[ \begin{array}{c} \bar{x}} \\ \bar{y} \\ \bar{z} \end{array} \right] = M_{lx} * \left[ \begin{array} {c}\bar{\rho} \\ \bar{\gamma} \\ \bar{\beta} \end{array} \right] \end{equation*}

with CMFs and CFs in 3xN format, M_{lx} a 3×3 matrix and * matrix multiplication.  Et voilà:[1]

Figure 1.  Solid lines: CIE (2012) 2° XYZ “physiologically-relevant” Colour Matching Functions and photopic Luminous Efficiency Function (V) from cvrl.org, the Colour & Vision Research Laboratory at UCL.  Dotted lines: The Cone Fundamentals in Figure 2 after linear transformation by 3×3 matrix Mlx below.  Source: cvrl.org.

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Deconvolution, Sharpening, unsharp mask, USM

The Richardson-Lucy Algorithm

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Deconvolution by the Richardson-Lucy algorithm is achieved by minimizing the convex loss function derived in the last article

(1)   \begin{equation*} J(O) = \sum \bigg (O**PSF - I\cdot ln(O**PSF) \bigg) \end{equation*}

with

  • J, the scalar quantity to minimize, function of ideal image O(x,y)
  • I(x,y), linear captured image intensity laid out in M rows and N columns, corrupted by Poisson noise and blurring by the PSF
  • PSF(x,y), the known two-dimensional Point Spread Function that should be deconvolved out of I
  • O(x,y), the output image resulting from deconvolution, ideally without shot noise and blurring introduced by the PSF
  • **   two-dimensional convolution
  • \cdot   element-wise product
  • ln, element-wise natural logarithm

In what follows indices x and y, from zero to M-1 and N-1 respectively, are dropped for readability.  Articles about algorithms are by definition dry so continue at your own peril.

So, given captured raw image I blurred by known function PSF, how do we find the minimum value of J yielding the deconvolved image O that we are after?

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ACR, Color, LR, Perceptual, Processing

Color: Determining a Forward Matrix for Your Camera

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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 →