A problem one can ofter encouter while shooting is the insufficient dynamic range of the
sensor. The dynamic range can be described as the range of brightness values which can be reproduced
in a correct manner. Toward higher brightness values it is limited in a rather sharp way
by the saturation of the photodiods, in other words by the moment in which the photodiods are
"full" and are not able any more to collect further light photons. At the level of the picture
it corrisponds to the areas which are burnt, it means completely white and whithout any detail.
Towerd lower brightness values the limit is less sharp, and it corrisponds to the brightness
value at which the signal becomes indistinguishable noise.
This means that it is not possible to improve the apparent dynamic range just by working on the
exposure curve, since in this way the darker parts would show inacceptable
amounts of noise when lightened.
An alternative solution is represented by the fusion of differently exposed pictures. Real HDR photography
(High Dynamic Range photography) is based on the modelling of the physical properties of the sensor
by the analysis of various pictures (at least three), in order to obtain the values of the parameters which characterize
its response to incoming light. Although valuable, this approach is very complicated. I belive
that, at least in some situations, a much more simple approach to the problem is possible, an
approach that completely avoids a modelling of the properties of the sensor and is just based on the fusion of two
pictures, according to a certain rule, in order to obtain a final image which better reproduces the real
observed scene than any possible simple single photo. This solution is proposed on the page
Dynamic Range Increaser
of this web site. The method is based on shooting two pictures
(obviously with exactly the same framing), one exposed for the brighter parts of the scene and one
exposed for the parts of the scene which appear too dark in the first one. The two pictures
are then fused, so that the information of the second picture progressively replaces the
information from the first, the more the area of the first picture gets darker.
Compared to real HDR this approach is simplicistic, nevertheless it can deliver good results
in most of the cases in which all parts of the scene can be correctly exposed at least in one of the
two starting pictures. In case of strongly contrasted scenes, like sunsets or windows in a room,
or scenes with particular light conditions, like reflections,
this can not be the case and real HDR would be necessary.
In the following an example. The picture shows a scene in which the trees on the sides risult
more or less agains the light. Exposing correctly for the central parts of the picture the branches
of the trees appear almost black, although in reality they appeared green, with many recognizable
Modifying the exposure curve it is possible to lighten the branches.
If you look in detail, however, you see that in the parts which have been lightened there is now
much more noise than in the rest or the picture and that the details are there not very well
Using instead a second picture, with a longer exposure time, so that the trees are correctly exposed,
and fusing it with the first one, according to a particular algorithm, described further forward,
it is possible to get a better result
in which the trees are at the same time correctly exposed, don' t show more noise than the rest of the picture
and possess a good amount of details.
If you want to use this page, these are the steps:
- Shoot a picture correctly exposed for the brighter parts of the scene (High Lights picture)
- Shoot a picture correctly exposed for the parts of the scene which appear too dark in the first picture (Low Lights Picture)
- Upload both picters. Obviously they must be of the same size and shot with identical frameset.
- Choose the best gradiend shape, position and width. You can get a preview of the result by
cliccking on "Preview!"
- Once you have found a suitable gradient you can get the proper mixed photo by clicking on "Execute Mixing!"
After a while in the lower part of the page a large preview will appear. Clicking on it you can download the
mixed picture at full resolution.
The parameters you can vary are the gradient shape, its width and the minimum brightess, it means the minimum
brightness for which all the information, at the level of the single pixel, comes from the High Lights picture.
The brightness of a pixel has been defined as the normalized square root of the some of the squares of the three RGB values.
If, for example, a pixel has RGB values 100, 150 and 200, respectively, the brightness
will be 0,61. The brightness of black is 0, that of white is 1.
The gradient can have four different shapes:
- Linear gradient
- Sigmoidal gradient
- Reversed linear gradient
- Reversed sigmoidal gradient
In the first case the percentage of information coming from the Low Light picture increases linearly
with the decreasing of the pixel brightness. In the second case, instead, it increases following an s-shaped curve.
In the case of the two inverted gradients the basic (sort of reference) picture is the one for the low
lights, to which information is replaced with data coming from the picture for the High Lights in
the brightest parts. The following pictures illustrate the four gradient types and the meaning of
the parameters you can work on:
Up to now I can say that the gradient shape which gives more often the best result is the linear gradient.
Often it is useful to set the gradient width larger then the minimum brightness. This means that
in the final image no picture will contain only information coming from the picture for the
On the final picture it is often useful to use a simple image editor (FastStone Image Viewer is pefect for this
purpose) in order to increase the local contrast and sometimes the
gamma value. Otherwise the picture tends to look a little bit dull and darker than the original.
Here some examples (by clicking on the previews you can download half resolution pictures):
High Lights picture
Low Lights picture
The page now permits to correct small vertical and horizontal shift between the two pictures.
Rotations have still to be corrected in advance with an image editing tool, like, for example,
FastStone Image Viewer
Usually softwares of this kind allow to rotate the image up to a precision of 0.1 degrees. This
precision is enough to obtain pictures perfect for internet, altough often not enough to eminate
all defects at full resolution.
The image which has to be rotated is always the one for the high lights (the darker one).
When you rotate it with the external tool be sure that the autocrop option is not active. Use the larger created image
without cutting away the created lateral triangles. The image is going to be cropped automatically to the
dimension of the low light pictures while uploading.
The correction system includes three options:
In this case the necessary corretion is evaluated based on a grid of nine rectangles uniformly
distributed on the image. It is the first way which was implemented, but is the less reliable.
In this case it is possible to decide the position and the dimensions of a rectangle on which estimate
the best shift. To do it, click once on the high light image to open it and double click to close it.
I have come to the conclusion that the best is to create a very large rectangle, which includes the whole image,
because this reduces the uncertainties of the system.
Two cautions: on one hand it is important not to include in the rectangle subjects which have moved from one
picture to the other. On the other hand it is also necessary to leave enough margins on all sides so
that the rectangles doesn't go out of the shifted image. If this happens, the system will encounter an error.
In this case just go back to the previous page with the browser and choose a smaller rectangle.
- Square to optimize manually the shift
In this case the square part of the two images are shown superposed at original dimension and in transparency
in a dedicated table. Clicking on the arrows it is possible to shift one square in respect to the other
until the subjects are exactly superposed. The values for the vertical and the horizontal shift can
be used to create the preview or directly the full size image (throuh the button Apply!).
The manual optimization can be necessary in case of images with no sharp edges inside or in which part
of the subject becomes invisible in the longer exposed picture.
Important Note: If the shown square doesn't look like it should, in particolar with Internet Explorer,
delate the cache of the browser!
The option "Use inserted values as starting point!" means that the optimization process (in case
of the first two options) starts from the inserted values. This is useful to help the algorythm move
in the correct directions when the shift is very large (for example in the case of pictures taken
at long focal lentgh).