Kevin Connery wrote:
I just finished reading John Russ’ Image Processing Handbook. My brain now hurts, even though I skimmed past many of the sections not directly applicable to 2D visible-light photography.

However, he did discuss the use of deconvolution as a sharpening technique, and I was wondering if anyone was familiar with any non-scientific utilities to perform this on digital images. Raindeer Graphic’s Fovea Pro has an interactive deconvolver, but the package is overkill (and at $800, a bit expensive for this purpose). The Refocus utility in their Optipix package ($150) seems reasonable, but I’m curious if anyone had experience with that product or a similar deconvolution tool.

From what he wrote, it seems as though it would be the best solution to removing the softening due to a digital camera's anti-aliasing filter--better than any of the common capture sharpening approaches.

ImageJ (started life as NIHImage) is a F/OSS scientific image processor that will do this and a bazillion other tricks.

http://rsbweb.nih.gov/ij/

RONALD N TAN wrote:
Has anyone played with the Custom filter?

Used to, but around v6 or so, I've been using the free Reindeer Graphic's version. It supports non-integer coefficients in an up to 7x7 neighborhood instead of the built-in 5x5 integer-only one. But that's convolution, not deconvolution.

I'll take a look at focusmagic and do some further digging. I hadn't realized Smart Sharpen included deconvolution--that, too, needs some research.

Thanks.

Keep 'em coming!

Paul Brecht wrote:
Unshake - free:

http://www.hamangia.freeserve.co.uk/

Paul

I've tried that, and while it produces some increase in clarity, it's effects are very limited.

Lumigraphics wrote:

ImageJ (started life as NIHImage) is a F/OSS scientific image processor that will do this and a bazillion other tricks.

http://rsbweb.nih.gov/ij/

That is a cool program, thanks for posting the link, and thanks Kevin for starting the thread. Always fun to find out how little I really know.

Kevin Connery wrote:
I just finished reading John Russ’ Image Processing Handbook.

Expensive book. Worth it?

nwprophoto wrote:

Expensive book. Worth it?

cheaper here!

http://usedmarketplace.borders.com/book … hn+C.+Russ

nwprophoto wrote:

Expensive book. Worth it?

Not unless you're a math geek or involved in image analysis. While the author works to keep the math load down, it's not going to be easy reading to anyone unfamiliar with at least the basics of image processing under the hood.

It's not aimed at photographers. It's aimed at forensic analysts, astronomers, biologists, and so on. Scientists, as users of images, who need to extract the most usable information from an image's underlying data. Many of the techniques are wonderful for isolating aspects of an image from what would be--in that situation--noise, but those techniques would in most cases destroy the image's value as a photograph. (Some examples would be removing texture from the surface under a fingerprint, or removing lines from the background of a check to permit computer comparisons of the handwriting, or edge detection, or element correlation, or...)

I've been following Jim Hoerricks' Photoshop Forensics blog, and figured there might be some techniques applicable to photography. There are, but most of them are most readily available via more user-friendly routes. Deconvolution, moire removal via FFT, 'top hat' noise handling (dust removal) and a few others still tend to be very uncommon. (But not entirely unknown: focusmagic, Optipix, ImageJ, and a few others support them.)

As for 'worth it': I got my copy from the library. It's useful, and I'm glad I read it, but it's not something I need to own. If I worked in a scientific lab, I'd probably want my own copy.

Sean Baker wrote:
I started to play with this a couple of weeks ago while trying to work with some badly OOF / motion-blurred shots which came my way.  I don't have all the sites I was referencing in front of me (will dig them up if there's interest), but my own opinion came to be that the Richardson-Lucy algorithm gave the cleanest + most effective results in deconvolving images for capture and / or output sharpening.  I might argue that some of the advantage which DMF users benefit from is a well-applied version of a similar algorithm in camera, before writing the raw data.  Raw Therapee is one free program which gives an R-L implementation up to 2.5px radius - good enough for most all of us, with the exception perhaps of Mr. Randall and his new toy .

FWIW (or more likely not), playing with all this has led me to modify my web output techniques markedly to use the following, stupidly complex, process.  I use this vs. R-L simply because it gives me a bit more control over how much I sharpen the finer details.
  1.) GB of original image at ( (original-long-axis resolution / new resolution) * 0.25)px. [reduce resizing artifact]
  2.) Resize (bicubic) to new resolution.
  3.) Duplicate image x2.
  4.) GB first (bottom) duplicate @ 0.3-0.6 (wherever I would expect USM to just give me artifacts).
  5.) Select second duplicate.
  6.) Apply Image - Subtract / GB'd layer as source / scale 2 / offset 128.
  7.) Set second duplicate to Linear Light.
  8.) Create Curves adjustment layer linked to second duplicate - points at I/O (1,0) + (255, 254); set to 50% opacity & merge.
  9.) Smart Sharpen second duplicate - 300-500% @ (0.5 * GB radius) / remove GB.
  10.) Smart Sharpen first duplicate to taste - 25-100% @ GB radius / remove GB.
  11.) Merge duplicate layers, set blend mode to Luminosity, adjust opacity to taste.

And yeah, I use an action for that.

Edit to note, the idea to separate the fine detail sharpening is not my own.

Edit 2: Action can be downloaded here.  It does assume that it is already working a duplicate copy of the final image.

Edit 3: Action is optimized for working in a 16bit environment and in current form the spatial frequency separation demonstrates only a peak 1/32767 variation from the original image.  If 8bit types would like help perfecting the sep without going to 16bit, drop a note and I'll be happy to share findings.

https://www.modelmayhem.com/po.php?thre … st11166541

Lumigraphics wrote:
ImageJ (started life as NIHImage) is a F/OSS scientific image processor that will do this and a bazillion other tricks.

http://rsbweb.nih.gov/ij/

I had never heard of Deconvolution or ImageJ.  I just stumbled on this thread, downloaded ImageJ and noodled around.  I couldn't find a "Deconvolve" command, but found the Filter>> Convolve.

I don't know if this qualifies as proper Deconvolve Sharpening but it did have a nice sharpening effect. 


Here's what I did:

1)  Open an image in ImageJ
2)  Process>> Filter>> Convolve        - The result looks similar to 'glowing edges' neon chalk drawing filter in Photoshop.
3) Save the image under a new name.
4) Close ImageJ and Open both the original and Convolved images in Photoshop.
5)  Using the original image as the background layer, copy and paste the Convolved image onto Layer 2.
6)  Set layer 2 blend mode to "Soft light" and Opacity to about 5-10%  Opacity needs to be set carefully to avoid a glowing halo.

Final result looks like the original with a pretty decent edge sharpening.    I am not sure how it stacks up to other sharpening techniques yet.  Is anybody else sharpening photos this way?



Sample PSD file available for download here:
http://www.pixoasis.com/va.php?hash=18d … 6c96f3b04b

Warren Leimbach wrote:
I had never heard of Deconvolution or ImageJ.  I just stumbled on this thread, downloaded ImageJ and noodled around.  I couldn't find a "Deconvolve" command, but found the Filter>> Convolve.

I don't know if this qualifies as proper Deconvolve Sharpening but it did have a nice sharpening effect.

Deconvolution is simply convolution with a reverse kernel.  The original image was convolved with an convolution kernel to blur it and applying the reverse kernel will deblur it.  However, some information is lost in the process, so we can't expect perfection.

The original convolution might be performed by the lens or other optical components ("protectant" filters for example), or atmospheric haze or heat turbulence, etc.  A lens is a kind of parallel optical computer, performing a computation on each ray of light simultaneously.

There is a technique called homomorphic deconvolution that I studied many years back in university.  With it, one can analyze the image and automatically extract some information from it about the function that blurred it and use that information to construct a deconvolution kernel to sharpen the image.  In examples, unreadable license plate numbers became readable.  It applied to gaussian blur types as well as motion blurring.

Russ's book is quite good as a compendium of techniques.  I got a copy a few years ago.

Monito -- Alan wrote:
Deconvolution is simply convolution with a reverse kernel.

Interesting.  So how do I reverse a kernal?


In ImageJ when I choose Process>>Filter>> Convolve  I see this grid:

-1 -1 -1 -1 -1
-1 -1 -1 -1 -1
-1 -1 24 -1 -1
-1 -1 -1 -1 -1
-1 -1 -1 -1 -1

  Is that the kernal?

If I input different values, say positive numbers instead of the negative numbers, will that "reverse" the kernal?

Warren Leimbach wrote:
Interesting.  So how do I reverse a kernal?

Yes, the number array is the kernel (note spelling).

I don't know, sorry.  It's been a while since I worked with any of that stuff and I can't find my copy of Russ.  I think it is non-simple to invert a kernel, because it seems that some kernels lead to numerical instabilities if they are inverted.