Exercise 6 - seeing red

The exercise required: a scene which has a wide range of brightness - so I chose a cold and frosty morning with a bright winter sun hiding behind the trees.

Path around the lake

This image was taken facing into the sun and clearly suffers from burn out in and around the trees on the left hand side. But rather than being limited to just the 'disk' of the sun, the burnt out area extends a good way cross the sky. In this image it is possible to determine the area between nearly-white and total white - whether one could draw a line along the exact edge is doubtful.

When looking at a close up section of the image, even with the clipped version along side, it would be difficult to define the edge.

Whole image with clipping warning

The red above is the highlight clipping warning showing approximately half of the sky is burnt out. What cannot be clearly seen in this web-photograph is that there are also numerous little red dots in the grass where information has been lost. 


The sun

Conversely, the image above is a close up of the sun from the under exposed version of the photograph and this red spot is the limit of the burn out. However, to reduce it this far the rest of the image is very dark - to make this scene work as a photograph and number of different exposures would need to be blended together.

Information loss - close up of the trees at the end of the warehouse



Information retained

The two photographs above show the same trees - the first is from the over exposed image (+2), the second form the under exposed image (-2). Not only is branch detail retained, but also the crossing vapour trails.


Purple fringing

This purple fringing, also known as chromatic aberration and is caused because each colour of light has its own refractive index meaning it bends at a different angle. This results in some wavelengths hitting the camera sensor in exactly right and others being slightly off - either in front of, or behind. This fringing is most noticeable across the light/dark transition.

With regards colour saturation - the over exposed images look pale and washed out, whereas the under exposed images are dark (rather than saturated). Ironically, I often find that photographs taken in harsh light can look as though they very taken in hazy conditions.



Final image







Exercise 5 - the root of all noise?

Take any picture and convert it to 16 bits per channel.

Starting Cow

Add an adjustment layer and apply a curve to compensate for the camera's sensor linear processing algorithm.


Linear adjustment curve

Save the new darker image and compare it with the original.


Dark Cow

Application of the adjustment curve has resulted in the histogram being skewed significantly to the left hand side.
Now apply a second adjustment curve to return the image to its original state.


Gamma correction curve

This curve removes the skew applied to the histogram and the image looks normal again.

Final Cow

The histogram of the final image is not exactly the same as the original one, this is because the two curves I applied to the image were not exact opposites. 

Looking at the histograms of the images above: -

Ø         Dark cow - in the original image 'seen' by the sensor, the shadow information takes up approximately one fifth of the histogram area

Ø         Final cow - the same shadow information has been 'stretched and moved' and covers approximately two thirds of the histogram area.

Thus, it is hardly surprising that when this linear processing takes place, any noise in the shadow area is 'magnified' as the algorithm guesses to fill in the blanks and make up the new area under the curve.