Category Archives: Sharpening

DOF, Optics, Sensors, Sharpening, Sharpness, Spatial Resolution

Diffracted DOF Aperture Guides: 24-35mm

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As a landscape shooter I often wonder whether old rules for DOF still apply to current small pixels and sharp lenses. I therefore roughly measured  the spatial resolution performance of my Z7 with 24-70mm/4 S in the center to see whether ‘f/8 and be there’ still made sense today.  The journey and the diffraction-simple-aberration aware model were described in the last few posts.  The results are summarized in the Landscape Aperture-Distance charts presented here for the 24, 28 and 35mm focal lengths.

I also present the data in the form of a simplified plot to aid making the right compromises when the focusing distance is flexible.  This information is valid for the Z7 and kit in the center only.  It probably just as easily applies to cameras with similarly spec’d pixels and lenses. Continue reading Diffracted DOF Aperture Guides: 24-35mm →

Deconvolution, Sharpening, unsharp mask, USM

The Richardson-Lucy Algorithm

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Deconvolution by the Richardson-Lucy algorithm is achieved by minimizing the convex loss function derived in the last article

(1)   \begin{equation*} J(O) = \sum \bigg (O**PSF - I\cdot ln(O**PSF) \bigg) \end{equation*}

with

  • J, the scalar quantity to minimize, function of ideal image O(x,y)
  • I(x,y), linear captured image intensity laid out in M rows and N columns, corrupted by Poisson noise and blurred by the PSF
  • PSF(x,y), the known two-dimensional Point Spread Function that should be deconvolved out of I
  • O(x,y), the output image resulting from deconvolution, ideally without shot noise and blurring introduced by the PSF
  • **   two-dimensional convolution
  • \cdot   element-wise product
  • ln, element-wise natural logarithm

In what follows indices x and y, from zero to M-1 and N-1 respectively, are dropped for readability.  Articles about algorithms are by definition dry so continue at your own peril.

So, given captured raw image I blurred by known function PSF, how do we find the minimum value of J yielding the deconvolved image O that we are after?

Continue reading The Richardson-Lucy Algorithm →

Deconvolution, Processing, Sharpening

Elements of Richardson-Lucy Deconvolution

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We have seen that deconvolution by naive division in the frequency domain only works in ideal conditions not typically found in normal photographic settings, in part because of shot noise inherent in light from the scene. Half a century ago William Richardson (1972)[1] and Leon Lucy (1974)[2] independently came up with a better way to deconvolve blurring introduced by an imaging system in the presence of shot noise. Continue reading Elements of Richardson-Lucy Deconvolution →

Deconvolution, IQ, Optics, Sharpening, Sharpness, Spatial Resolution

Capture Sharpening: Estimating Lens PSF

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The next few articles will outline the first tiny few steps towards achieving perfect capture sharpening, that is deconvolution of an image by the Point Spread Function (PSF) of the lens used to capture it.  This is admittedly  a complex subject, fraught with a myriad ever changing variables even in a lab, let alone in the field.  But studying it can give a glimpse of the possibilities and insights into the processes involved.

I will explain the steps I followed and show the resulting images and measurements.  Jumping the gun, the blue line below represents the starting system Spatial Frequency Response (SFR)[1], the black one unattainable/undesirable perfection and the orange one the result of part of the process outlined in this series.

Figure 1. Spatial Frequency Response of the imaging system before and after Richardson-Lucy deconvolution by the PSF of the lens that captured the original image.

Continue reading Capture Sharpening: Estimating Lens PSF →

adobe camera raw, Color, LR, Processing, Sharpening

How Is a Raw Image Rendered?

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What are the basic low level steps involved in raw file conversion?  In this article I will discuss what happens under the hood of digital camera raw converters in order to turn raw file data into a viewable image, a process sometimes referred to as ‘rendering’.  We will use the following raw capture by a Nikon D610 to show how image information is transformed at every step along the way:

Nikon D610 with AF-S 24-120mm f/4 lens at 24mm f/8 ISO100, minimally rendered from raw as outlined in the article.
Figure 1. Nikon D610 with AF-S 24-120mm f/4 lens at 24mm f/8 ISO100, minimally rendered from raw by Octave/Matlab following the steps outlined in the article.

Rendering = Raw Conversion + Editing

Continue reading How Is a Raw Image Rendered? →

Banding, Information Science, IQ, Perceptual, Processing, Quantization, Sensors, Sharpening, USM

Image Quality: Raising ISO vs Pushing in Conversion

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In the last few posts I have made the case that Image Quality in a digital camera is entirely dependent on the light Information collected at a sensor’s photosites during Exposure.  Any subsequent processing – whether analog amplification and conversion to digital in-camera and/or further processing in-computer – effectively applies a set of Information Transfer Functions to the signal  that when multiplied together result in the data from which the final photograph is produced.  Each step of the way can at best maintain the original Information Quality (IQ) but in most cases it will degrade it somewhat.

IQ: Only as Good as at Photosites’ Output

This point is key: in a well designed imaging system** the final image IQ is only as good as the scene information collected at the sensor’s photosites, independently of how this information is stored in the working data along the processing chain, on its way to being transformed into a pleasing photograph.  As long as scene information is properly encoded by the system early on, before being written to the raw file – and information transfer is maintained in the data throughout the imaging and processing chain – final photograph IQ will be virtually the same independently of how its data’s histogram looks along the way.

Continue reading Image Quality: Raising ISO vs Pushing in Conversion →

Deconvolution, IQ, Sharpening, Spatial Resolution

Downsizing Algorithms: Effects on Resolution

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Most of the photographs captured these days end up being viewed on a display of some sort, with at best 4K (4096×2160) but often no better than HD resolution (1920×1080).  Since the cameras that capture them have typically several times that number of pixels, 6000×4000 being fairly normal today, most images need to be substantially downsized for viewing, even allowing for some cropping.  Resizing algorithms built into browsers or generic image viewers tend to favor expediency over quality, so it behooves the IQ conscious photographer to manage the process, choosing the best image size and downsampling algorithm for the intended file and display medium.

When downsizing the objective is to maximize the original spatial resolution retained while minimizing the possibility of aliasing and moirè.  In this article we will take a closer look at some common downsizing algorithms and their effect on spatial resolution information in the frequency domain.

Continue reading Downsizing Algorithms: Effects on Resolution →

Deconvolution, Processing, Sharpening, Spatial Resolution

Raw Converter Sharpening with Sliders at Zero?

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I’ve mentioned in the past that I prefer to take spatial resolution measurements directly off the raw information in order to minimize often unknown subjective variables introduced by demosaicing and rendering algorithms unbeknownst to the operator, even when all relevant sliders are zeroed.  In this post we discover that that is indeed the case for ACR/LR process 2010/2012 and for Capture NX-D – while DCRAW appears to be transparent and perform straight out demosaicing with no additional processing without the operator’s knowledge.

Continue reading Raw Converter Sharpening with Sliders at Zero? →

Equivalence, Formats, IQ, Perceptual, Sharpening, Spatial Resolution

Can MTF50 be Trusted?

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A reader suggested that a High-Res Olympus E-M5 Mark II image used in the previous post looked sharper than the equivalent Sony a6000 image, contradicting the relative MTF50 measurements, perhaps showing ‘the limitations of MTF50 as a methodology’.   That would be surprising because MTF50 normally correlates quite well with perceived sharpness, so I decided to check this particular case out.

‘Who are you going to believe, me or your lying eyes’?

Continue reading Can MTF50 be Trusted? →

IQ, Perceptual, Sharpening, Spatial Resolution

Why Raw Sharpness IQ Measurements Are Better

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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.

Can we do that off a file rendered by a raw converter or, heaven forbid, a Jpeg?  Not quite, especially if the objective is measuring IQ. Continue reading Why Raw Sharpness IQ Measurements Are Better →

IQ, Perceptual, Sharpening, Spatial Resolution

How Sharp are my Camera and Lens?

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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?

In the next four articles I will walk you through my methodology based on captures of slanted edge targets:

  1. The setup (this one)
  2. Why you need to take raw captures
  3. The Slanted Edge method explained
  4. The software to obtain MTF curves

Continue reading How Sharp are my Camera and Lens? →

Deconvolution, Sharpening, Spatial Resolution

What is the Best Single Deconvolution PSF to Use for Capture Sharpening 1?

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Deconvolution is one of the  processes by which we can attempt to undo blurring introduced by our hardware while capturing an image.  It can be performed in the spatial domain via a kernel or in the frequency domain by dividing image data by one or more  Point Spread Functions.  The best single deconvolution PSF to use when Capture Sharpening is the one that resulted in the blurring in the first place: the System PSF.   It is often not easy or practical to determine it. Continue reading What is the Best Single Deconvolution PSF to Use for Capture Sharpening 1? →

Deconvolution, Sharpening, Spatial Resolution

Point Spread Function and Capture Sharpening

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A Point Spread Function is the image projected on the sensing plane when our cameras are pointed at a single, bright, infinitesimally small Point of light, like a distant star on a perfectly dark and clear night.   Ideally, that’s also how it would appear on the sensing material (silicon) of our camera sensors: a singularly small yet bright point of light surrounded by pitch black.  However a PSF can never look like a perfect point because in order to reach silicon it has to travel at least through an imperfect lens (1) of finite aperture (2), various filters (3) and only then finally land typically via a microlens on a squarish photosite of finite dimensions (4).

Each time it passes through one of these elements the Point of light is affected and spreads out a little more in slightly different ways, so that by the time it reaches silicon it is no longer a perfect Point but it is a slightly blurry Point instead: the image that this spread out Point makes on the sensing material is called the System’s Point Spread Function.  It is what we try to undo through Capture Sharpening. Continue reading Point Spread Function and Capture Sharpening →

Sharpening, Spatial Resolution

Deconvolution PSF Changes with Aperture

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We have  seen in the previous post how the radius for deconvolution capture sharpening by a Gaussian PSF can be estimated for a given setup in well behaved and characterized camera systems.  Some parameters like pixel aperture and AA strength should remain stable for a camera/prime lens combination as f-numbers are increased (aperture is decreased) from about f/5.6 on up – the f/stops dear to Full Frame landscape photographers.  But how should the radius for generic Gaussian deconvolution  change as the f-number increases from there? Continue reading Deconvolution PSF Changes with Aperture →

Deconvolution, IQ, Sharpening, Spatial Resolution

What Radius to Use for Deconvolution Capture Sharpening

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The following approach will work if you know the spatial frequency at which a certain MTF relative energy level (e.g. MTF50) is achieved by your camera/lens combination as set up at the time that the capture was taken.

The process by which our hardware captures images and stores them  in the raw data inevitably blurs detail information from the scene. Continue reading What Radius to Use for Deconvolution Capture Sharpening →

Deconvolution, IQ, Perceptual, Sharpening, Spatial Resolution

Deconvolution vs USM Capture Sharpening

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UnSharp Masking (USM) capture sharpening is somewhat equivalent to taking a black/white marker and drawing along every transition in the picture to make it stand out more – automatically.  Line thickness and darkness is chosen arbitrarily to achieve the desired effect, much like painters do. Continue reading Deconvolution vs USM Capture Sharpening →