This article will introduce a new approach for sharpening with the Smart Sharpen filter, which does not use masks or selections and minimises excessive noise amplification and distracting halos. The method outlined below makes use of a 25+ year old image processing technique that may be unknown to many Photoshop users, in order to facilitate the application and improve the results of the Smart Sharpen filter. Although the sharpening properties of this approach are best suited for acquisition/capture and monitor viewing sharpening, it is also possible to exaggerate the effect to produce wider, less subtle halos which are suitable for output sharpening for halftone or stochastic based print.
Sharpening is one of the most popular and subjective topics in modern digital image processing, with many people developing a wide range of competing sharpening workflows; actions and other tools. Sharpening results and the various options that can be employed in a sharpening workflow are often discussed and debated with great enthusiasm in online forums and email lists. In Photoshop CS2, Adobe introduced the "Smart Sharpen" filter, which was a major addition to the existing sharpening options which had not seen any updates with new version releases over the years. Smart Sharpen uses an undisclosed "Deconvolution" algorithm to remove blur in a mathematical attempt to restore focus to an image, rather than simply adding edge contrast to blurred pixels to simulate focus.
Smart Sharpen was initially met with interest, however many Photoshop users reverted to using their older, trusted Unsharp Mask, Band Pass or Difference of Gaussians based sharpening techniques. The lack of a "Threshold" or other noise avoidance option in Smart Sharpen is often viewed from two extreme opposite viewpoints from users familiar with traditional sharpening methods. One camp considers Smart Sharpen to be smart as it does not have a Threshold type command, while the other camp views the lack of a Threshold type command to be a serious design flaw - as noise and sharp, fine detail are often overly amplified. Even with further modifications such as masks or various blending options, Smart Sharpen may not be employed to it's full potential when used with common editing approaches.
Smart Sharpen - Basics
Unlike the Unsharp Mask filter, Smart Sharpen requires viewing at 100% (1:1 pixel) magnification. Giving the Smart Sharpen filter a cursory view, there are familiar Amount and Radius sliders, without a Threshold option. There is the added choice of three different blur types, with optional tonal blending options similar to those found in the Shadow/Highlight command. With simple application, one may conclude that results are the same as Unsharp Mask. This is true when Smart Sharpen is set to Basic mode, Gaussian Blur with the "More Accurate" option disabled. Results are identical to USM of the same values with zero Threshold.
When the More Accurate checkbox is enabled, the Smart Sharpen filter becomes smart, using a processor intensive iterative deconvolution process to remove blur to improve acutance. Of note, when run in More Accurate mode, visible artifacts may be introduced with some images when the filter is applied to 8 bpc data. It is strongly recommended that filtering be performed in 16 bpc mode, even if the image was in 8 bpc mode prior to running Smart Sharpen (even more so if the image is grayscale or has had JPEG compression applied).
Deconvolution and the Point Spread Function (PSF)
The Point Spread Function describes the convolution (blur) in the image and is critical to successful deconvolution. The closer that the PSF describes the image blur, the more accurate the final result. In astronomy, a blurred star or other point light source provides an ideal measure of the image blur, however in regular photography it may be much harder to assess the image blur. Smart Sharpen offers three simple blur types with fixed parameters: Gaussian Blur, Lens Blur and directional Motion Blur.
Gaussian Blur is the "Swiss Army Knife" of the Smart Sharpen filter. Simple blur in natural images will likely be different to the Gaussian weighting, however the Gaussian function can often be successfully used as a generic substitute with the knowledge that better results could be obtained with a more accurate PSF description.
Lens Blur differs from Gaussian Blur and only provides accurate results when the image blur matches the Lens Blur PSF description. Once the appropriate Amount and Radius settings are found, Lens Blur results should be compared against the Gaussian Blur results. One may find that the Gaussian PSF will provide more pleasing results, even if the blur is truly lens related.
Motion Blur removal is controlled via an interface similar to the Blur/Motion Blur filter, with a choice of directional blur angle. Selecting the correct blur angle is critical, otherwise noticeable artifacts may be generated with little appreciable restoration of focus.
Smart Sharpen - Review
Smarter Sharpen Technique - Introduction
As previously noted, without a Threshold type command, Smart Sharpen using the More Accurate option can be very unforgiving when it comes to image noise, lossy compression damage and high spatial frequency detail. When the More Accurate option is disabled, results are the same as USM, which defeats the purpose of using Smart Sharpen over traditional methods. Traditional approaches to using the Smart Sharpen filter often process both low and high frequencies using the same settings. I believe that successful use of the Smart Sharpen filter requires the targeting of various ranges of spatial frequency, with different Amount and Radius values appropriate to each frequency range.
In the spirit of the Adobe naming convention, I have named this technique Smarter Sharpen. More descriptive terms may include "Bi-Frequency Decomposition & Deconvolution" or "Iterative Multi-Scale Focus Restoration". To my knowledge, this method has not been publicly proposed for dedicated use with the Smart Sharpen filter prior to the publication of this article in December 2008.
The Smarter Sharpen technique targets the various image spatial frequencies by "decomposing" or splitting the original image data into a layer stack of two images, with the low frequency (Gaussian) data in the lower layer and the high frequency (Laplacian) data in the upper layer. This bi-frequency decomposition forms part of a classic image processing technique known as "Gaussian & Laplacian Pyramid Processing" (however the Smarter Sharpen technique does not use a decreasing resolution pyramid structure). Once the image has been decomposed into separate high and low frequency layers, Smart Sharpening can be applied with greater success than when processing regular image data.
Smarter Sharpen, Basic Technique - Gaussian & Laplacian Decomposition
Stage 1: Bi-Frequency Decomposition
Creating the Gaussian and Laplacian bi-frequency data layers is a fairly simple process. When performed in 16 bpc mode, the decomposition is lossless, resulting in no change to the original image when it is recombined. This is critical, as even the "negligible" 0.5 standard deviation error in 8 bpc mode processing is enough to generate artifacts when iterative decomposition is performed.
Presuming that one is working on a flattened duplicate image or history snapshot in 16 bpc mode:
Stage 2: Low Frequency Deconvolution & Supplemental Sharpening
In a basic single decomposition Smarter Sharpen workflow, the low frequency data layer is deconvolved with the Smart Sharpen filter using the Gaussian + More Accurate option, with appropriate Amount and Radius settings for the image. If the low frequency layer was created with a 2 pixel radius blur, then an appropriate initial radius for the Smart Sharpen Gaussian removal filter could be around 1.7 pixels. Next, gradually increase the radius value by steps of 0.1 or 0.2 pixels until the low frequency layer "pops" into better focus, without amplifying noise or adding objectionable halos. For a 2 pixel Gaussian Blur, the radius "sweet spot" setting may be around 1.8 to 2.2 pixels (dependent on the sharpness and noise levels of the original image). This is a general guide only, depending on image variables one may need to select lower or higher values. When in doubt, a lower Radius and Amount setting may be appropriate.
The following options further enhance the image sharpness over the initial application of the Smart Sharpen filter to the low frequency data.
Stage 3: Flattening and Blending
The final stage is to "collapse" or Flatten the sharpened low and high frequency layers into a regular composite image. The sharpened layer is then blended over the original colour image in Luminosity layer blending mode. Opacity may be globally reduced, or the Layer Option Blend If sliders and or layer masks may be incorporated to further control the blended results of the Smarter Sharpen technique.
Smarter Sharpen, Advanced Technique - Iterative Multi-scale Deconvolution
Smarter Sharpen - Processing Variables
Smarter Sharpen Technique - Review
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