HDR in Microsoft Excel?! Kevin Chen !!Con 2017 Motivation N/A - - PowerPoint PPT Presentation

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HDR in Microsoft Excel?! Kevin Chen !!Con 2017 Motivation N/A - - PowerPoint PPT Presentation

Nov 01, 2023 243 likes •624 views

HDR in Microsoft Excel?! Kevin Chen !!Con 2017 Motivation N/A Background brighten and replace? Reciprocity Open the shutter 2x longer 2x more light 2x 2x brighten and replace? 250 200 150 100 50 0 10 5 0 5

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HDR in Microsoft Excel?!

Kevin Chen • !!Con 2017

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Motivation

  • N/A
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Background

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brighten and replace?

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Reciprocity

Open the shutter 2x longer ➡ 2x more light

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brighten and replace? 2x 2x

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Nonlinearity

2x more light " 2x bigger number in JPG

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☀

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Recovering High Dynamic Range Radiance Maps from Photographs Paul E. Debevec Jitendra Malik

University of California at Berkeley ABSTRACT We present a method of recovering high dynamic range radiance maps from photographs taken with conventional imaging equip-
  • ment. In our method, multiple photographs of the scene are taken
with different amounts of exposure. Our algorithm uses these dif- ferently exposed photographs to recover the response function of the imaging process, up to factor of scale, using the assumption of reci-
  • procity. With the known response function, the algorithm can fuse
the multiple photographs into a single, high dynamic range radiance map whose pixel values are proportional to the true radiance values in the scene. We demonstrate our method on images acquired with both photochemical and digital imaging processes. We discuss how this work is applicable in many areas of computer graphics involv- ing digitized photographs, including image-based modeling, image compositing, and image processing. Lastly, we demonstrate a few applications of having high dynamic range radiance maps, such as synthesizing realistic motion blur and simulating the response of the human visual system. true measurements of relative radiance in the scene. For example, if
  • ne pixel has twice the value of another, it is unlikely that it observed
twice the radiance. Instead, there is usually an unknown, nonlinear mapping that determines how radiance in the scene becomes pixel values in the image. This nonlinear mapping is hard to know beforehand because it is actually the composition of several nonlinear mappings that occur in the photographic process. In a conventional camera (see Fig. 1), the film is first exposed to light to form a latent image. The film is then developed to change this latent image into variations in trans- parency, or density, on the film. The film can then be digitized using a film scanner, which projects light through the film onto an elec- tronic light-sensitive array, converting the image to electrical volt-
  • ages. These voltages are digitized, and then manipulated before fi-
nally being written to the storage medium. If prints of the film are scanned rather than the film itself, then the printing process can also introduce nonlinear mappings. In the first stage of the process, the film response to variations in exposure (which is , the product of the irradiance the
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2x more light 2x more light 31 78 183

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log(amount of light) digital number

☀

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$ = f(☀)

digital number = f(brightness • shutter open time)

Z = f(E · ∆t)

flip it!

f −1(Z) = E · ∆t g(Z) = E · ∆t

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A × x = b

❓ ✅ ✅ ✕ =

Very Big Matrix™

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A × x = b

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A × x = b x = A+ × b

Pseudo Inverse Matrix

If the columns of a matrix A are linearly independent, so AT· A is invertible and we obtain with the following formula the pseudo inverse: A+ = (AT · A)-1 · AT Here A+ is a left inverse of A , what means: A+· A = E . One of the most popular Shareware Math Programs in Germany

MatheAss

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Microsoft Excel Time™

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This helper sheet calculates the pixel coordinates that we will use in recovering g(Z). There are a total

  • f 49 values that form a 7x7 grid.
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x = A+ × b

One of the most popular Shareware Math Programs in Germany

MatheAss

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uses g(Z) to get light intensity

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kevinchen.co