A number of interesting insights come to light once one realizes that as far as the slanted edge method (of measuring the Modulation Transfer Function of a Bayer CFA digital camera and lens from its raw data) is concerned it is as if it were dealing with identical images behind three color filters, each in their own separate, full resolution color plane:
In this and the previous article I discuss how Modulation Transfer Functions (MTF) obtained from every raw color plane of a Bayer CFA in isolation can be combined to provide an objective and meaningful composite MTF curve for the imaging system as a whole. There are two main ways to accomplish this goal:
) that reflects the mix of spectral information captured in the raw data, divorced from downstream color science; and
) that reflects the luminance channel of the image as neutrally displayed, hence may be more perceptually relevant.Both are valid on their own, though the weights of the former are fixed for any Bayer sensor while the latter are scene, camera/lens and illuminant dependent. For this reason I usually prefer input-referred weights as a first pass when comparing cameras and lens hardware in similar conditions. Continue reading System MTF from Bayer Sensors
In this and the following article I will discuss my thoughts on how MTF50 results obtained from raw data of the four Bayer CFA color channels off a neutral target captured with a typical camera through the slanted edge method can be combined to provide a meaningful composite MTF50 for the imaging system as a whole. The perimeter of the discussion are neutral slanted edge measurements of Bayer CFA raw data for linear spatial resolution (‘sharpness’) photographic hardware evaluations. Corrections, suggestions and challenges are welcome. Continue reading Combining Bayer CFA Modulation Transfer Functions – I
For the purposes of ‘sharpness’ spatial resolution measurement in photography cameras can be considered shift-invariant, linear systems when capturing scene detail of random size and direction such as one often finds in landscapes.
Shift invariant means that the imaging system should respond exactly the same way no matter where light from the scene falls on the sensing medium . We know that in a strict sense this is not true because for instance pixels tend to have squarish active areas so their response cannot be isotropic by definition. However when using the slanted edge method of linear spatial resolution measurement we can effectively make it shift invariant by careful preparation of the testing setup. For example the edges should be slanted no more than this and no less than that. Continue reading Linearity in the Frequency Domain