Tag Archives: exposure

Exposure, imaging system, irradiance, luminance, radiance, Radiometry, reflectance, Uncategorized

Angles and the Camera Equation

Leave a comment

Imagine a bucolic scene on a clear sunny day at the equator, sand warmed by the tropical sun with a typical irradiance (E) of about 1000 watts per square meter.  As discussed earlier we could express this quantity as illuminance in lumens per square meter (lx) – or as a certain number of photons per second (\Phi) over an area of interest (\mathcal{A}).

(1)   \begin{equation*} E = \frac{\Phi}{\mathcal{A}} \; (W, lm, photons/s) / m^2 \end{equation*}

How many photons/s per unit area can we expect on the camera’s image plane (irradiance E_i )?

Figure 1.  Irradiation transfer from scene to sensor.

In answering this question we will discover the Camera Equation as a function of opening angles – and set the stage for the next article on lens pupils.  By the way, all quantities in this article depend on wavelength, which will be assumed in the formulas to make them more readable.

Continue reading Angles and the Camera Equation →

Color, Exposure, Photometry, Radiometry, Sensors

Connecting Photographic Raw Data to Tristimulus Color Science

Leave a comment

Absolute Raw Data

In the previous article we determined that the three r_{_L}g_{_L}b_{_L} values recorded in the raw data in the center of the image plane in units of Data Numbers per pixel – by a digital camera and lens as a function of absolute spectral radiance L(\lambda) at the lens – can be estimated as follows:

(1)   \begin{equation*} r_{_L}g_{_L}b_{_L} =\frac{\pi p^2 t}{4N^2} \int\limits_{380}^{780}L(\lambda) \odot SSF_{rgb}(\lambda) d\lambda \end{equation*}

with subscript _L indicating absolute-referred units and SSF_{rgb} the three system Spectral Sensitivity Functions.   In this series of articles \odot is wavelength by wavelength multiplication (what happens to the spectrum of light as it progresses through the imaging system) and the integral just means the area under each of the three resulting curves (integration is what the pixels do during exposure).  Together they represent an inner or dot product.  All variables in front of the integral were previously described and can be considered constant for a given photographic setup. Continue reading Connecting Photographic Raw Data to Tristimulus Color Science →

Color, Exposure, Photometry, Radiometry, Sensors

The Physical Units of Raw Data

Leave a comment

In the previous article we (I) learned that the Spectral Sensitivity Functions of a given digital camera and lens are the result of the interaction of light from the scene with all of the spectrally varied components that make up the imaging system: mainly the lens, ultraviolet/infrared hot mirror, Color Filter Array and other filters, finally the photoelectric layer of the sensor, which is normally silicon in consumer kit.

Figure 1. The journey of light from source to sensor.  Cone Ω will play a starring role in the narrative that follows.

In this one we will put the process on a more formal theoretical footing, setting the stage for the next few on the role of white balance.

Continue reading The Physical Units of Raw Data →

Color, Exposure, Photometry, Radiometry, Sensors

The Spectral Response of Digital Cameras

2 Comments

Photography works because visible light from one or more sources reaches the scene and is reflected in the direction of the camera, which then captures a signal proportional to it.  The journey of light can be described in integrated units of power all the way to the sensor, for instance so many watts per square meter. However ever since Newton we have known that such total power is in fact the result of the weighted sum of contributions by every frequency  that makes up the light, what he called its spectrum.

Our ability to see and record color depends on knowing the distribution of the power contained within a subset of these frequencies and how it interacts with the various objects in its path.  This article is about how a typical digital camera for photographers interacts with such a spectrum from the scene: we will dissect what is sometimes referred to as the system’s Spectral Response or Sensitivity.

Figure 1. Spectral Sensitivity Functions of an arbitrary imaging system, resulting from combining the responses of the various components described in the article.

Continue reading The Spectral Response of Digital Cameras →

Color, Formats, Processing, Raspberry Pi, Sensors

Opening Raspberry Pi High Quality Camera Raw Files

17 Comments

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 →

Banding, Information Science, IQ, Perceptual, Processing, Quantization, Sensors, Sharpening, USM

Image Quality: Raising ISO vs Pushing in Conversion

2 Comments

In the last few posts I have made the case that Image Quality in a digital camera is entirely dependent on the light Information collected at a sensor’s photosites during Exposure.  Any subsequent processing – whether analog amplification and conversion to digital in-camera and/or further processing in-computer – effectively applies a set of Information Transfer Functions to the signal  that when multiplied together result in the data from which the final photograph is produced.  Each step of the way can at best maintain the original Information Quality (IQ) but in most cases it will degrade it somewhat.

IQ: Only as Good as at Photosites’ Output

This point is key: in a well designed imaging system** the final image IQ is only as good as the scene information collected at the sensor’s photosites, independently of how this information is stored in the working data along the processing chain, on its way to being transformed into a pleasing photograph.  As long as scene information is properly encoded by the system early on, before being written to the raw file – and information transfer is maintained in the data throughout the imaging and processing chain – final photograph IQ will be virtually the same independently of how its data’s histogram looks along the way.

Continue reading Image Quality: Raising ISO vs Pushing in Conversion →

Exposure, Information Science, IQ, Uncategorized

Information Transfer – The ISO Invariant Case

6 Comments

We know that the best Information Quality possible collected from the scene by a digital camera is available right at the output of the sensor and it will only be degraded from there.  This article will discuss what happens to this information as it is transferred through the imaging system and stored in the raw data.  It will use the simple language outlined in the last post to explain how and why the strategy for Capturing the best Information or Image Quality (IQ) possible from the scene in the raw data involves only two simple steps:

1) Maximizing the collected Signal given artistic and technical constraints; and
2) Choosing what part of the Signal to store in the raw data and what part to leave behind.

The second step is only necessary  if your camera is incapable of storing the entire Signal at once (that is it is not ISO invariant) and will be discussed in a future article.  In this post we will assume an ISOless imaging system.

Continue reading Information Transfer – The ISO Invariant Case →

Exposure, Photometry, Radiometry, Sensors

How Many Photons on a Pixel at a Given Exposure

2 Comments

How many photons impinge on a pixel illuminated by a known light source during exposure?  To answer this question in a photographic context under daylight we need to know the effective area of the pixel, the Spectral Power Distribution of the illuminant and the relative Exposure.

We can typically estimate the pixel’s effective area and the Spectral Power Distribution of the illuminant – so all we need to determine is what Exposure the relative irradiance corresponds to in order to obtain the answer.

Continue reading How Many Photons on a Pixel at a Given Exposure →

Exposure, Radiometry

Photons Emitted by Light Source

6 Comments

How many photons are emitted by a light source? To answer this question we need to evaluate the following simple formula at every wavelength in the spectral range of interest and add the values up:

(1)   \begin{equation*} \frac{\text{Power of Light in }W/m^2}{\text{Energy of Average Photon in }J/photon} \end{equation*}

The Power of Light emitted in W/m^2 is called Spectral Exitance, with the symbol M_e(\lambda) when referred to  units of energy.  The energy of one photon at a given wavelength is

(2)   \begin{equation*} e_{ph}(\lambda) = \frac{hc}{\lambda}\text{ joules/photon} \end{equation*}

with \lambda the wavelength of light in meters and h and c Planck’s constant and the speed of light in the chosen medium respectively.  Since Watts are joules per second the units of (1) are therefore photons/m^2/s.  Writing it more formally:

(3)   \begin{equation*} M_{ph} = \int\limits_{\lambda_1}^{\lambda_2} \frac{M_e(\lambda)\cdot \lambda \cdot d\lambda}{hc} \text{ $\frac{photons}{m^2\cdot s}$} \end{equation*}

Continue reading Photons Emitted by Light Source →

Exposure, Perceptual, Photometry

Converting Radiometric to Photometric Units

Leave a comment

When first approaching photographic science a photographer is often confused by the unfamiliar units used.  In high school we were taught energy and power in radiometric units like watts ($W$) – while in photography the same concepts are dealt with in photometric units like lumens ($lm$).

Once one realizes that both sets of units refer to the exact same physical process – energy transfer – but they are fine tuned for two slightly different purposes it becomes a lot easier to interpret the science behind photography through the theory one already knows.

It all boils down to one simple notion: lumens are watts as perceived by the Human Visual System.

Continue reading Converting Radiometric to Photometric Units →

Exposure, Information Science, IQ, Sensors

Exposure and ISO

Leave a comment

The in-camera ISO dial is a ballpark milkshake of an indicator to help choose parameters that will result in a ‘good’ perceived picture. Key ingredients to obtain a ‘good’ perceived picture are 1) ‘good’ Exposure and 2) ‘good’ in-camera or in-computer processing. It’s easier to think about them as independent processes and that comes naturally to you because you shoot raw in manual mode and you like to PP, right? Continue reading Exposure and ISO →

Exposure

What Is Exposure

2 Comments

When capturing a typical photograph, light from one or more sources is reflected from the scene, reaches the lens, goes through it and eventually hits the sensing plane.

In photography Exposure is the quantity of visible light per unit area incident on the image plane during the time that it is exposed to the scene.  Exposure is intuitively proportional to Luminance from the scene $L$ and exposure time $t$.  It is inversely proportional to lens f-number $N$ squared because it determines the relative size of the cone of light captured from the scene.  You can read more about the theory in the article on angles and the Camera Equation.

Continue reading What Is Exposure →