Identification : Focus

Introduction

While there are numerous different mechanisms that can affect image focus, the actual cause of a focus error is perhaps of less importance for our purposes than the extent to which an image is out of focus.  For simplicity I only use three focus increments in the Image Quality Tool:-

(1) The image looks perfectly sharp in which case edges are crisp and well defined.

(2) The image looks soft.  The image is reasonably usable but the edges are not totally crisp.

(3) The image is out of focus.  The whole image is very soft and details are hard to make out.

It is the combination of image resolution and acutance that make up what we term image sharpness as neatly explained HERE.  So, for the best focus we need a reasonably high pixel resolution plus a reasonable level of edge contrast, or acutance.  I have already dealt with image resolution HERE so I am going to focus on acutance here, while also taking a look at image sharpening tools.

Raw Image Softening

As outlined HERE there is a little more to digital image focus than meets the eye.  While an image may appear perfectly pin-sharp through the lens, the RAW image formed by the camera will always start out softer due to an image processing step called demosaicing.  This image softness is then corrected automatically using an unsharp mask algorithm, though this step can alternatively be completed manually using Camera Raw or some other raw viewing software.  

Unsharp Mask

Unsharp Mask basically makes an image appear sharper by increasing edge contrast or acutance.  This may or may not adversely impact an image from a bird identification perspective.  Usually, a little image-sharpening has little or no adverse affect at the macro level but does undoubtedly alter the appearance of fine details and the edges between objects.  So, for example, use of the unsharp mask could subtly change the colour or contrast of a very narrow feather edge or other micro structures.  Like a lot of image editing tools, when the unsharp mask is overused there are a number of additional problems including the introduction or worsening of some image artefacts.  I think, from the point of view of correctly scoring focus and focus related artefacts using the Image Quality Tool it is important to go into some of these artefacts in rather more detail.  For more on the use of unsharp mask see HERE.

Edge Halo Artefacts

A telltale sign that Unsharp Mask may have been overused, halo artefacts are a bi-product of the mask itself.  The mask is effectively a high-pass filter and the halos are like echoes produced by filtering of the data.  For more on the technical aspects see HERE.

From a practical perspective, image halos may be white or coloured and obviously can give the impression of false feather fringes and other false plumage markings.  Because they tend to be of the order of a couple of pixels in width they are only likely to confuse an identification at low pixel resolutions but clearly there is a potential to confuse things.  However, with experience it is possible to recognise artificial image sharpening.  At the level that halos are becoming obvious, the image contrast and acutance tends to look unnaturally high.

Aliasing and Blurring Artefacts

There is an excellent tutorial on the Cambridge in Colour website HERE that explains a linkage between three related digital image artefacts and how they interact during image sharpening.  As one of the consequences of correcting for demosaicing, Unsharp Mask or a similar algorithm may be used to try and improve image quality and sharpness.  

In lower resolution images, jagged, pixillated edges can be smoothed by simply blurring the image, but the whole image appears soft.  "Nearest Neighbour" algorithms simply average pixels either side of a border, creating a smoother surface appearance - termed aliasing.   Finding the right balance between blurring, aliasing and sharpening without creating halos often requires the aid of an human eye, experienced in using unsharp mask.  Simply applying automatic sharpening is liable to lead to poor results and unwanted artefacts, especially with lower resolution images.

Many photographic websites present this type of diagram to illustrate the trade-offs during image sharpening.  The images below compare the different effects of the unsharp mask on high resolution and low resolution images.  In this case, the original image was slightly low in acutance so the sharpening tool has certainly increased sharpness, or at least, the appearance of sharpness.  Overall contrast is increased. Pale sharpening halos are evident around the artificially added sharp black edges and around the bird's bill.  There is a dark halo created at the border between the white breast and the darker patches of water behind.  While the overall effect is to increase contrast and acutance, in this case the bird also appears to jump out of the background - pseudo 3D-style.  This can be another encouragement to overuse of unsharp mask.

With a lower resolution image (below) there should be obvious pixilation visible but the particular software used has blurred the image intentionally to mask the pixilation.  As a result, the effect of a much lower resolution is not particularly noticeable at full screen resolution.  Interestingly the unsharp mask actually has the effect of undoing some of the blurring and revealing the pixilation in this image, so there isn't a great advantage to increasing acutance in this case.   Additionally, the sharpened image again contains sharpening halos to a similar extent as the high resolution image.  Moiré becomes more obvious at this resolution but is only apparent in the artificially added focus wheel.  Moiré occurs wherever there are fine, regular patterns.  In bird images it most commonly affects the regular pattern of flight feather edges on a closed wing.

I could keep going and talking about the individual pros and cons of different sharpening tools but what counts here in terms of a bird identification is whether or not the critical details and colours can be judged accurately.  In the Image Quality Tool there is an opportunity to score down poor resolution and artefacts separately from focus.  The key in my view to deciding if an image is sharp, soft or out of focus for identification purposes is the effect on fine detail.  If important fine details are blurred to the point they can't be reliably seen the image is out of focus.  If the details are not perfectly clear the image is soft.  Otherwise, I would tend score the image as being sharp.

See also HERE.