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?
Point Spread Function and Capture Sharpening
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
Deconvolution PSF Changes with Aperture
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
What Radius to Use for Deconvolution Capture Sharpening
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 vs USM Capture Sharpening
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
What Is Exposure
When capturing a typical photograph, light from one or more sources is reflected from the scene, reaches the lens, goes through it and eventually hits the sensing plane.
In photography Exposure is the quantity of visible light per unit area incident on the image plane during the time that it is exposed to the scene. Exposure is intuitively proportional to Luminance from the scene $L$ and exposure time $t$. It is inversely proportional to lens f-number $N$ squared because it determines the relative size of the cone of light captured from the scene. You can read more about the theory in the article on angles and the Camera Equation.