This post will continue looking at the spatial frequency response measured by MTF Mapper off slanted edges in DPReview.com raw captures and relative fits by the ‘sharpness’ model discussed in the last few articles. The model takes the physical parameters of the digital camera and lens as inputs and produces theoretical directional system MTF curves comparable to measured data. As we will see the model seems to be able to simulate these systems well – at least within this limited set of parameters.
The following fits refer to the green channel of a number of interchangeable lens digital camera systems with different lenses, pixel sizes and formats – from the current Medium Format 100MP champ to the 1/2.3″ 18MP sensor size also sometimes found in the best smartphones. Here is the roster with the cameras as set up:
Now that we know from the introductory article that the spatial frequency response of a typical perfect digital camera and lens can be modeled simply as the product of the Modulation Transfer Function of the lens and pixel area, convolved with a Dirac delta grid at cycles-per-pixel spacing
While perusing Jim Kasson’s excellent Longitudinal Chromatic Aberration tests I was impressed by the quantity and quality of the information the resulting data provides. Longitudinal, or Axial, CA is a form of defocus and as such it cannot be effectively corrected during raw conversion, so having a lens well compensated for it will provide a real and tangible improvement in the sharpness of final images. How much of an improvement?
This is a vast and complex subject for which I do not have formal training. In this and the previous article I present my thoughts on how MTF50 results obtained from raw data of the four Bayer CFA channels off a uniformly illuminated neutral target captured with a typical digital camera through the slanted edge method can be combined to provide a meaningful composite MTF50 for the imaging system as a whole1. Corrections, suggestions and challenges are welcome. Continue reading COMBINING BAYER CFA MTF Curves – II→
This is a vast and complex subject for which I do not have formal training. 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 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→
Over the last couple of years I’ve been using Frans van den Bergh‘s excellent open source MTF Mapper to measure the Modulation Transfer Function of imaging systems off a slanted edge target, as you may have seen in these pages. As long as one understands how to get the most out of it I find it a solid product that gives reliable results, with MTF50 typically well within 2% of actual in less than ideal real-world situations (see below). I had little to compare it to other than to tests published by gear testing sites: they apparently mostly use a commercial package called Imatest for their slanted edge readings – and it seemed to correlate well with those.
Then recently Jim Kasson pointed out sfrmat3, the matlab program written by Peter Burns who is a slanted edge method expert who worked at Kodak and was a member of the committee responsible for ISO12233, the resolution and spatial frequency response standard for photography. sfrmat3 is considered to be a solid implementation of the standard and many, including Imatest, benchmark against it – so I was curious to see how MTF Mapper 0.4.1.6 would compare. It did well.
Olympus just announced the E-M5 Mark II, an updated version of its popular micro Four Thirds E-M5 model, with an interesting new feature: its 16MegaPixel sensor, presumably similar to the one in other E-Mx bodies, has a high resolution mode where it gets shifted around by the image stabilization servos during exposure to capture, as they say in their press release
‘resolution that goes beyond full-frame DSLR cameras. 8 images are captured with 16-megapixel image information while moving the sensor by 0.5 pixel steps between each shot. The data from the 8 shots are then combined to produce a single, super-high resolution image, equivalent to the one captured with a 40-megapixel image sensor.’
A great idea that could give a welcome boost to the ‘sharpness’ of this handy system. This preliminary test shows that the E-M5 mk II 64MP High-Res mode gives in this case a 10-12% advantage in MTF50 linear spatial resolution compared to the Standard Shot 16MP mode. Plus it apparently virtually eliminates the possibility of aliasing and moiré. Great stuff, Olympus.
So, is it true that a Four Thirds lens needs to be about twice as ‘sharp’ as its Full Frame counterpart in order to be able to display an image of spatial resolution equivalent to the larger format’s?
It is, because of the simple geometry I will describe in this article. In fact with a few provisos one can generalize and say that lenses from any smaller format need to be ‘sharper’ by the ratio of their sensor linear sizes in order to produce the same linear resolution on same-sized final images.
This is one of the reasons why Ansel Adams shot 4×5 and 8×10 – and I would too, were it not for logistical and pecuniary concerns.
You have obtained a raw file containing the image of a slanted edge captured with good technique. How do you get the MTF curve of the camera and lens combination that took it? Download and feast your eyes on open source MTF Mapper by Frans van den Bergh. No installation required, simply store it in its own folder.
My preferred method for measuring the spatial resolution performance of photographic equipment these days is the slanted edge method. It requires a minimum amount of additional effort compared to capturing and simply eye-balling a pinch, Siemens or other chart but it gives immensely more, useful, accurate, quantitative information in the language and units that have been used to characterize optical systems for over a century: it produces a good approximation to the Modulation Transfer Function of the two dimensional Point Spread Function of the camera/lens system in the direction perpendicular to the edge.
Much of what there is to know about a system’s spatial resolution performance can be deduced by analyzing such a curve, starting from the perceptually relevant MTF50 metric, discussed a while back. And all of this simply from capturing the image of a black and white slanted edge, which one can easily produce and print at home.
Why Raw? The question is whether one is interested in measuring the objective, quantitative spatial resolution capabilities of the hardware or whether instead one would prefer to measure the arbitrary, qualitatively perceived sharpening prowess of (in-camera or in-computer) processing software as it turns the capture into a pleasing final image. Either is of course fine.
My take on this is that the better the IQ captured the better the final image will be after post processing. In other words I am typically more interested in measuring the spatial resolution information produced by the hardware comfortable in the knowledge that if I’ve got good quality data to start with its appearance will only be improved in post by the judicious use of software. By IQ here I mean objective, reproducible, measurable physical quantities representing the quality of the information captured by the hardware, ideally in scientific units.
You want to measure how sharp your camera/lens combination is to make sure it lives up to its specs. Or perhaps you’d like to compare how well one lens captures spatial resolution compared to another you own. Or perhaps again you are in the market for new equipment and would like to know what could be expected from the shortlist. Or an old faithful is not looking right and you’d like to check it out. So you decide to do some testing. Where to start? Continue reading How Sharp are my Camera and Lens?→
Is MTF50 a good proxy for perceived sharpness? It turns out that the spatial frequencies that are most closely related to our perception of sharpness vary with the size and viewing distance of the displayed image.
For instance if an image captured by a Full Frame camera is viewed at ‘standard’ distance (that is a distance equal to its diagonal) the portion of the MTF curve most representative of perceived sharpness appears to be around MTF90. Continue reading MTF50 and Perceived Sharpness→
The following approach will work if you know the MTF50 in cycles/pixel of your camera/lens combination as set up at the time that the capture you’d like to sharpen by deconvolution with a Gaussian PSF was taken.