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11 LANGFORD’S ADVANCED PHOTOGRAPHY

increase in contrast and shallower a decrease (Figure 11.15(c) and (d). Using a larger number of
selected points allows a high degree of local control, however it is important to keep on checking
the effect on the image, as too many ‘wiggles’ are not necessarily a good thing: in the top part of
the curve in Figure 11.15(d), the distinctive bump actually indicates a reversal of tones.

Using curves to correct a colour cast
This is where having an understanding of basic colour theory is useful. Because the image is
made up of only three (or four) colour channels, then most colour casts can be corrected by
using one of these. Look at the image and identify what the main hue of the colour cast is. From
this, you can work out which colour channel to correct. Both the primary and its complementary
colour will be corrected by the same colour channel:

Colour cast Correction channel
Red or Cyan Red
Green or Magenta Green
Blue or Yellow Blue

(a) (b) Artefacts as a
result of tone or
(c) colour corrections
Figure 11.16 (a) Original image. Posterized image (b) and its histogram (c). As with all image
processes, overzealous
application of any of
these methods can result
in certain unwanted
effects in the image.
Obvious casts may be
introduced as a result of
overcorrecting one
colour channel
compared to the others.
Overexpansion of the
tonal range in any part
can result in missing
values and a posterized
image (see Figure 11.16).
Lost levels cannot be
retrieved without
undoing the operation,
therefore should be
avoided by applying
corrections in a more
moderate way and by
using 16 bit images
wherever possible.

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Another possible effect is the clipping of values at either end of the range, which will result in loss of
shadow detail and burning out of highlights and will show as a peak at either end of the histogram.

Filtering operations

Both noise removal and image sharpening are generally applied using filtering. Spatial filtering
techniques are neighbourhood operations, where the output pixel value is defined as some
combination or selection from the neighbourhood of values around the input value. The methods
discussed here are limited to the filters used for correcting images, not the large range of special
effects creative filters in the filter menu of image editing software such as Adobe Photoshop.

The filter (or mask) is simply a range of values which are placed over the neighbourhood
around the input pixel. In linear filtering the values in the mask are multiplied by the values in the
image at neighbourhood at the same point and the result is added together and sometimes
averaged. Blurring and sharpening filters are generally of this type. Non-linear filters simply use the
mask to select the neighbourhood. Instead of multiplying the neighbourhood with mask values, the
selected pixels are sorted and a value from the neighbourhood output, depending on the operation
being applied. The median filter is an example, where the median value is output, eliminating very
high or low values in the neighbourhood, making it very successful for noise removal.

Noise removal
There are a range of both linear and non-linear filters available for removing different types of
noise and specially adapted versions of these may also be built-in to the software of capture
devices. Functions such as digital ICE™ for suppression of dust and scratches in some scanner
software are based on adaptive filtering methods. The linear versions of noise removal filters
tend to be blurring filters, and result in edges being softened; therefore care must be taken when
applying them (Figure 11.17). Non-linear filters such as the median filter, or the ‘dust and

(a) (b)

Figure 11.17 Filtering artefacts: (a) Original image (b) Noise removal
filters can cause blurring and posterization, and oversharpening can
cause a halo effect at the edges. This is clearly shown in (c) which
(c) illustrates a magnified section of the sharpened image.

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speckles’ filter in Photoshop are better at preserving edges, but can result in posterization if
applied too heavily.

Sharpening
Sharpening tends to be applied using linear filters. Sharpening filters emphasize edges, but may
also emphasize noise, which is why sharpening is better performed after noise removal. The
unsharp mask is a filter based upon a method used in the darkroom in traditional photographic
imaging, where a blurred version of the image is subtracted from a boosted version of the
original, producing enhanced edges. This can be successful, but again care must be taken not to
oversharpen. As well as boosting noise, oversharpening produces a characteristic ‘overshoot’ at
edges, similar to adjacency effects, known as a halo artefact (Figure 11.17 (c)). For this reason
sharpening is better performed using layers, where the effect can be carefully controlled.

Digital colour

Although some early colour systems used additive mixes of red, green and blue, colour in
film-based photography is predominantly produced using subtractive mixes of cyan,
magenta and yellow (Chapter 4). Both systems are based on trichromatic matching, i.e.
colours are created by a combination of different amounts of three pure colour primaries.

Digital input devices and computer displays operate using additive RGB colour. At the print
stage, cyan, magenta and yellow dyes are used, usually with black (the key) added to account for
deficiencies in the dyes and improve the tonal range. In modern printers, more than three
colours may be used (six or even eight ink printers are now available and the very latest models
by Canon and Hewlett Packard use 10 or 12 inks to increase the colour gamut), although they are
still based on a CMY(K) system (see Chapter 10).

An individual pixel will therefore usually be defined by three (or four, in the case of CMYK)
numbers defining the amount of each primary. These numbers are coordinates in a colour space.
A colour space provides a three-dimensional (usually) model into which all possible colours may
be mapped (see Figure 11.18). Colour spaces allow us to visualize colours and their relationship
to each other spatially (see Chapter 4). RGB and CMYK are two broad classes of colour space,
but there are a range of others, as already encountered, some of which are much more specific
and defined than others (see next section). The colour space defines the axes of the coordinate
system, and within this, colour gamuts of devices and materials may then be mapped; these are
the limits to the range of colours capable of being reproduced.

The reproduction of colour in digital imaging is therefore more complex than that in
traditional silver halide imaging, because both additive and subtractive systems are used at
different stages in the imaging chain. Each device or material in the imaging chain will have a
different set of primaries. This is one of the major sources of variability in colour reproduction.
Additionally, colour appearance is influenced by how devices are set up and by the viewing
conditions. All these factors must be taken into account to ensure satisfactory colour. As an
image moves through the digital imaging chain, it is transformed between colour spaces and
between devices with gamuts of different sizes and shapes: this is the main problem with colour
in digital imaging. The process of ensuring that colours are matched to achieve adequately
accurate colour and tone reproduction requires colour management.

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Lightness (L) Colour spaces

White Colour spaces may be divided

G broadly into two categories:
device-dependent and device-

Y independent spaces. Device-

RM dependent spaces are native to

C White Y B input or output devices. Colours

GC specified in a device-dependent

R space are specific to that device,
they are not absolute. Device-
Black independent colour spaces specify
colour in absolute terms. A pixel
M Black Saturation (S) specified in a device-independent
Greyscale Hue angle (H) colour space should appear the
B same, regardless of the device on
HSL which it is reproduced.
RGB

Figure 11.18 RGB and HSL: colour spaces are multi-dimensional coordinate

systems in which colours may be mapped R (red), G (green), B (blue), M (magenta),

C (cyan) and Y (yellow).

Device-dependent spaces are defined predominantly by the primaries of a particular device,

but also by the characteristics of the device, based upon how it has been calibrated. This means,

for example, that a pixel with RGB values of 100, 25 and 255, when displayed on two monitors

from different manufacturers, will probably be displayed as two different colours, because in

general the RGB primaries of the two devices will be different. Additionally, as seen in Chapter 4,

the colours in output images are also affected by the viewing conditions. Even two devices of the

same model from the same manufacturer will produce two different colours if set up differently

(see Figure 11.19). RGB and CMYK are generally device dependent, although they can be

standardized to

become device

independent under

certain conditions

(sRGB is an example).

Device-

independent colour

spaces are derived

(a) (b) from CIEXYZ
colourimetry, i.e. they
Figure 11.19 Pixel values, when specified in device-dependent colour spaces will appear as are based on the
different colours on different devices. response of the

human visual system. CIELAB and CIELUV are examples. sRGB is actually a device calibrated

colour space, specified for images displayed on a cathode-ray tube (CRT) monitor, if the monitor

and viewing environment are correctly set up, then the colours will be absolute and it acts as a

device-independent colour space.

A number of common colour spaces separate colour information from tonal information,

having a single coordinate representing tone, which can be useful for various reasons. Examples

include hue, saturation and lightness (HSL) (see Figure 11.18) and CIELAB (see page 78); in both

cases the Lightness channel (L) represents tone. In such cases, often only a slice of the colour space

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1 CRT display may be displayed for clarity; the colour
Adobe RGB (1998) coordinates will be mapped in two dimensions
0.9 at a single lightness value, as if looking down
the lightness axis, or at a maximum chroma
0.8 regardless of lightness. Two-dimensional
CIELAB and CIE xy diagrams are examples
0.7 commonly used in colour management.
G
y
0.6

0.5

0.4 W Colour gamuts
0.3
0.2 R The colour gamut of a particular device
0.1 B defines the possible range of colours that the
device can produce under particular
conditions. Colour gamuts are usually

0 displayed for comparison in a device-
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 independent colour space such as CIELAB
or CIE Yxy. Because of the different
x technologies used in producing colour, it is
highly unlikely that the gamuts of two
Figure 11.20 Gamut mismatch: The gamuts of different devices devices will exactly coincide. This is known
are often different shapes and sizes, leading to colours that are out- as gamut mismatch (see Figure 11.20). In this
of-gamut for one device or the other. In this example, the gamut of
an image captured in the Adobe RGB (1998) colour space is wider
than the gamut of a CRT display.

case, some colours are within the gamut of one device but lie outside that of the other; these

colours tend to be the more saturated ones. A decision needs to be made about how to deal

with these out-of-gamut colours. This is achieved using rendering intents in an ICC colour

management system. They may be clipped to the boundary of the smaller gamut, for example,

leaving all other colours unchanged, however this means that the relationships between colours

will be altered and that many pixel values may become the same colour, which can result in

posterization. Alternatively, gamut compression may be implemented, where all colours are

shifted inwards, becoming less saturated, but maintaining the relative differences between the

colours and so achieving a more natural result.

Colour management systems

A colour management system is a software module which works with imaging applications
and the computer operating system to communicate and match colours through the imaging
chain. Colour management reconciles differences between the colour spaces of each device and
allows us to produce consistent colours. The aim of the colour management system is to convert
colour values successfully between the different colour spaces so that colours appear the same
or acceptably similar, at each stage in the imaging chain. To do this the colours have to be
specified first.

The conversion from the colour space of one device to the colour space of another is
complicated. As already discussed, input and output devices have their own colour spaces;
therefore colours are not specified in absolute terms. An analogy is that the two devices speak
different languages: a word in one language will not have the same meaning in another language
unless it is translated; additionally, words in one language may not have a direct translation in
the other language. The colour management system acts as the translator.

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ICC colour management

In recent years, colour management has been somewhat simplified with the development of
standard colour management systems by the International Color Consortium (ICC). ICC colour
management systems have four main components:

1. Profile connection space (PCS)
The PCS is a device-independent colour space, generally CIEXYZ, or CIELAB which is used as a software ‘hub’,
into which all colours are transformed into and out of. Continuing with the language analogy, the PCS is a
central language and all device languages are translated into the PCS and back again: there is no direct
translation from one device to another.

2. ICC Profiles
A profile is a data file containing information about the colour reproduction capabilities of a device, such as a
scanner, a digital camera, a monitor or a printer. There are also a number of intermediate profiles, which are not
specific to a device, but to a colour space, usually a working colour space. The profiling of a device is achieved by
calibration and characterization. These processes produce the information necessary for mapping colours between
the device colour space and the PCS. The ICC provides a standard format for these profile files, which allows them to
be used by different devices and across different platforms and applications. This means that an image can be
embedded with the profile from its capture device and when imported into any computer running an ICC colour
managed system, the colours should be correctly reproduced. Images can also be assigned profiles, or converted
between profiles (see later section on using profiles).

3. Colour management module (CMM)
The colour management module is the software ‘engine’ which performs all the calculations for colour
conversions. The CMM is separate from the imaging application. There are a number of standard ones for both
Mac and PC, which can be selected through the system colour management settings.

4. Rendering intents
Rendering intents define what happens to colours when they are out-of-gamut. The rendering intent is selected
by the user at a point when an image is to be converted between two colour spaces, for example when
converting between profiles, or when sending an RGB image to print. There are four ICC specified rendering
intents, optimized for different imaging situations. These are: perceptual, saturation, relative colourimetric and
absolute colourimetric. Generally, for most purposes, you will only use perceptual and relative colourimetric; the
other two are optimized for saturated graphics and for proofing in a printing press environment respectively and
are less likely to give a satisfactory result for everyday imaging. Previewing when converting or printing will allow
you to select the best one for your image.

Fundamental concepts

ICC colour management requires a bit of work in setting up and understanding how it works,
but provides an elegant solution to a complicated problem. In summary:

● Profiles provide information about colour spaces. The information in the file allows correct conversion of colour
values between the particular colour space and the PCS.

● The image is assumed to have a profile associated with it at all stages in the imaging chain. The profile may be
that of an input or output device, or a working space profile.

● If an image enters the workspace without a profile, then a profile may be assigned to it. This does not alter the
underlying pixel values.

● Images may be converted between profiles at any point in the imaging chain. Conversion will change the image
values, but should not alter the image appearance.

● Each conversion between profiles requires a source and a destination profile.

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