Having been an occasional user of Hugin for many years, I have described my recent experience of stitching High-dydnamic-range (HDR) and normal, (Low-dynamic-range) panoramas from a set of 9 images shot one evening at Salford Quays. The article should prove interesting and useful to anyone new to Hugin, or to those, like me, who use Hugin infrequently and never quite become “experts”.
As usual, Hugin did an excellent job of stitching, but I recommend outputting an HDR file in EXR format for tone-mapping in, for example, Luminance HDR.
After writing two articles on the Nature of Light and its relevance to digital photography, I found that the subject of noise still fascinated me and decided that I had to make some measurements. Looking at the wiggly waveforms of my previous article might indicate that camera A is noisier than camera B but can we measure the noise in a rigorous way? This present article explains how to do that using free software. As well as presenting graphs of the measurements I have attempted to explain the results from physical principles – and evidently the noise is predominantly photon noise (aka shot noise).
I have updated the page on this subject in order to clarify the distinction between calibration and profiling (or characterisation), and to remove reference to commercial operating systems now obsolete.
To avoid confusion I have now included only a version of each test card with sRGB profiles assigned and saved as jpegs. These should be viewed in an application that is colour aware (i.e. one that recognises and uses the embedded profile).
In the second of two articles I look at another natural phenomenon, photon noise (also known as Shot noise). As with diffraction blur, the problem becomes more serious as the physical size of the sensor is reduced.
Whilst this is not the only source of noise, it is now the dominant one in the darker areas of an image where only a relatively small number of photons are incident on the sensor. It is the counting of photons, which is subject to Poisson statistics, which produces the noise.
Reducing the physical size of a camera, even if the total number of pixels is maintained, inevitably reduces the quality of the images because of two fundamental properties of light itself. This technical article looks at diffraction, usually explained by considering light as waves. A future article will look at photon noise, explained by considering light as particles.
A simple rule-of-thumb is established for determining the “diffraction limited f-number” by relating this to pixel pitch on the sensor.
The earlier, pre-digital, criterion for diffraction limited aperture (based on required print sharpness) is revisited and considered to be still valid – perhaps with a little sharpening.
Basic adjustment of monitors and projection systems
When I last updated my page on this subject (2012) I decided that it was best to offer the test cards as .png files and leave it to the user to assign a profile (presumably sRGB) and to view the result in an application such as Photoshop. For convenience, I have now added a version of each test card with sRGB profiles assigned and saved as jpegs. These should be viewed in an application that is colour aware (i.e. one that recognises and uses the embedded profile).
Comparison of Demosaicing Methods available in Free, Open Source Raw Processors
My previous article included a table listing the various demosaicing algorithms offered by the four raw processors considered and I wondered why we (as users) needed such a wide choice. The table is reproduced below.I decided to investigate those offered by RawTherapee by looking closely at the detail in an image of tree branches against the sky – the same part of the same raw file processed by each of the algorithms.