{"id":609,"date":"2009-01-26T10:00:52","date_gmt":"2009-01-26T10:00:52","guid":{"rendered":"http:\/\/www.knowhowtransfer.com\/?p=609"},"modified":"2016-03-28T09:22:41","modified_gmt":"2016-03-28T09:22:41","slug":"notes-on-sharpening","status":"publish","type":"post","link":"https:\/\/www.knowhowtransfer.com\/store\/notes-on-sharpening\/","title":{"rendered":"Notes on Sharpening"},"content":{"rendered":"
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Sharpening with Gaussian and edge-aware blurring kernels; a new experimental approach on High-Radius Low-Amount sharpening; how to separately target edges and texture in the same high frequency range. Gaussian, Bilateral and Mixed pyramid decompositions, efficient platforms on the top of which new sharpening strategies can be developed.<\/em><\/p>\n

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By Davide Barranca<\/em><\/p>\n<\/blockquote>\n

\"davide_barranca\"1. Introduction<\/h3>\n

This article is a work in progress collection of personal notes on the subject of sharpening; I\u2019m no digital image processing scientist so, even though I like to play with theoretical problems, I try to \ufb01nd my answers within Photoshop (as I suppose they all do in Dan Margulis\u2019 Color Theory Yahoo group, to which attention I \ufb01rst would like to turn this one). Chances are that I won\u2019t be rigorous too, my aim is to better understand the subject and share my \ufb01ndings with people who would like to integrate them, or simply give a feedback on the techniques exposed. If you\u2019re not interested in all the (very trivial indeed) math and graphs, feel free to skip to the how-to sections: nevertheless, I hope everything will be food for thoughts.<\/p>\n

What I’m suggesting with this article is, among the rest, the possibility to target with appropriate sharpening different image features, even if they belongs to the same spatial frequency range (plus an experimental approach to high-radius low-amount sharpening). Then I’m showing how to use bilateral and mixed pyramid decompositions to modulate the sharpening within all the image frequencies.<\/p>\n

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(Fig. 1.1) Before and after version, applying some of the tecniques exposed in this article.<\/p><\/div>\n

2. Gaussian sharpening<\/h3>\n

There\u2019s plenty of resources on the web about the subject so I skip much of the basics of Photoshop USM (UnSharpMasking) and its use, why we need it, how to build masks, etc. Let me only stress again that it\u2019s called UnSharp because it assumes the subtraction from the original picture of a blurred (unsharp) version. I\u2019ll use a lot the \u201csubtraction\u201d, so here it goes a quick reminder on it, and why sharpening and blurring are close relatives inside Filter \u2192 Sharpen \u2192 Unsharp Mask…<\/p>\n

A quick note for the reader: Grayscale images will be used throughout the article, to help us keeping the focus on tonal transitions. To mimic the effects with color pictures, use L of Lab or apply a solid white layer Color mode to have what Photoshop would consider a grayscale Luminosity version of your original. First picture (courtesy of my friend the photographer Roberto Bigano) is the starting point; then goes the blurred version and the subtraction:<\/p>\n

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(Fig. 2.1) Original picture: BW version of the painting by Paolo de Matteis \u201cLe sante Maria, Maddalena e Dorotea\u201d (detail) National Gallery of Cosenza, Italy (Photography \u00a9 Roberto Bigano).<\/p><\/div>\n

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(Fig. 2.2) Original pictures, Blurred version and their subtraction.<\/p><\/div>\n

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Adding the subtraction to the original makes the sharpened version:<\/p>\n

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(Fig. 2.3) Original pictures plus the difference on Fig. 2.2 gives the sharpened picture.<\/p><\/div>\n

To do this in Photoshop, I may use offset and scaling, which makes the difference channel look more familiar:<\/p>\n

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(Fig 2.4) Using a scaled version of the difference in Photoshop.<\/p><\/div>\n

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(Fig 2.5) Detail of the sharpened version using a Gaussian Blur with Radius 4.0.<\/p><\/div>\n

A more abstract way to visualize the effect of Gaussian Sharpening, that I personally \ufb01nd useful for comparison purposes, is to plot the signal intensity transition (black) and its blurred version (red), their difference (green), and the original plus the difference (thicker black):<\/p>\n

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(Fig 2.6) Intensity transition and Gaussian Sharpening.<\/p><\/div>\n

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The key concept of Gaussian Sharpening is that the difference between original and blurred version is exactly what will be enhanced. If it\u2019s used a blurring kernel (Gaussian Blur, GB, for instance) which softens edges and texture, then edges and texture will be more prominent when the difference layer will be applied to the original. Depending on the algorithm used and the processing of the blurred picture, we may end with a sharpening that affects separately different image features.
Before going any further, let me review Photoshop\u2019s Calculations and how to use it to sharpen. Open a picture, convert to the grayscale \ufb02avor you like the most and:<\/p>\n