Unsharp masking in ImageJ – changing parameters

In a previous post we looked at whether image blur could be fixed, and concluded that some of it could be slightly reduced, but heavy blur likely could not. Here is the image we used, showing blur at two ends of the spectrum.

Blur at two ends of the spectrum: heavy (left) and light (right).

Now the “Unsharp Masking” filter in ImageJ, is not terribly different from that found in other applications. It allows the user to specify a “radius” for the Gaussian blur filter, and a mask weight (0.1-0.9). How does modifying the parameters affect the filtered image? Here are some examples using a radius of 10 pixels, and a variable mask weight.

Radius = 10; Mask weight = 0.25
Radius = 10; Mask weight = 0.5
Radius = 10; Mask weight = 0.75

We can see that as the mask weight increases, the contrast change begins to affect the colour in the image. Our eyes may perceive the “rent K” text to be sharper in the third image with MW=0.75, but the colour has been impacted in such as way that the image aesthetics have been compromised. There is little change to the acuity of the “Mölle” text (apart from the colour contrast). A change in contrast can certainly improve the visibility of detail in the image (i.e. they are easier to discern), however maybe not their actual acuity. It is sometimes a trick of the eye.

What about if we changed the radius? Does a larger radius make a difference? Here is what happens when we use a radius of 40 pixels, and a MW=0.25.

Radius = 40; Mask weight = 0.25

Again, the contrast is slightly increased, and perceptual acuity may be marginally improved, but again this is likely due to the contrast element of the filter.

Note that using a small filter size, e.g. 3-5 pixels in a large image (12-16MP) will have little effect, unless there are features in the image that size. For example, in an image containing features 1-2 pixels in width (e.g. a macro image), this might be appropriate, however will likely do very little in a landscape image.


Optical blur and the circle of non-sharpness

Most modern cameras automatically focus a scene before a photograph is acquired. This is way easier than the manual focus that occurred in the ancient world of analog cameras. When part of a scene is blurry, then we consider this to be out-of-focus. This can be achieved in a couple of ways. One way is by means of using the Aperture Priority setting on a camera.  Blur occurs when there is a shallow depth of field. Opening up the aperture to f/2.8 allows in more light, and the camera will compensate with the appropriate shutter speed. It also means that objects not in the focus plane will be blurred. Another way is through manually focusing a lens.

Either way, the result is optical blur. But optical blur is by no means shapeless, and has a lot to do with a concept known as the circle of confusion (CoC). The CoC occurs when the light rays passing through the lens are not perfectly focused. It is sometimes known as the disk of confusioncircle of indistinctnessblur circle, or blur spot. CoC is also associated with the concept of Bokeh, which I will discuss in a later post. Although honestly – circle of confusion may not be the best term. In German the term used is “Unschärfekreis”, which translates to “circle of non-sharpness“, which inherently makes more sense.

A photograph is basically an accumulation of many points – which represent the exact points in the real scene. Light striking an object reflects off many points on the object, which are then redirected onto the sensor by  the lens. Each of these points is reproduced by the lens as a circle. When in focus, the circle appears as a sharp point, otherwise the out-of-focus region appears as circle to the eye. Naturally the “circle” normally takes the shape of the aperture, because the light passes through it. The following diagram illustrates the “circle of confusion“. A photograph is exactly sharp only on the focal plane, with more or less blur around it.  The amount of blur depends on an objects  distance from the focal plane. The further away, the more distinct the blur. The blue lines signify an object in focus. Both the red and purple lines show objects not in the focal plane, creating large circles of confusion (i.e. non-sharpness = blur).

The basics of the “circle of confusion”

Here is a small example. The photograph below is taken in Bergen, Norway. The merlon on the battlement is in focus with the remainder of the photograph beyond that blurry. Circles of confusion are easiest to spot as small bright objects on darker backgrounds. Here a small white sign becomes a blurred circle-of-confusion.

An example of the circle of confusion as a bright object.

Here is a second example, of a forest canopy, taken through focusing manually. The CoC are very prevalent.

An example where the whole image is composed of blur.

As we de-focus the image further, the CoC’s become larger, as shown in the example below.

As the defocus increases (from left to right), so too does the blur, and the size of the CoC.

Note that due to the disparity in blurriness in a photograph, it may be challenging to apply a “sharpening” filter to such an image.

Could blur be the new cool thing in photography?

For many years the concept of crisp, sharp images was paramount. It lead to the development of a variety of image sharpening algorithms to suppress the effect of blurring in an image. Then tilt-shift appeared, and was in vogue for a while (it’s still a very cool effect). Here blur was actually being introduced into an image. But what about actually taking blurry images?

I have been experimenting with adding blur to an image, either through the process of  manually defocusing the lens, or by taking a picture of a moving object. The results? I think they are just as good, if not better than if I had “stopped the motion”, or created a crisp photograph. We worry far too much about defining every single feature in an image, and too little on a bit of creativity. Sometimes it would be nice to leave something in an image that inspires thought.

Here’s an example of motion-blur, a Montreal Metro subway car coming into a platform. It is almost the inverse of tilt-shift. Here the object of interest is blurred, and the surround area is kept crisp. Special equipment needed? Zip.

Can blurry images be fixed?

Some photographs contain blur which is very challenging to remove. Large scale blur, which is the result of motion, or defocus can’t really be suppressed in any meaningful manner. What can usually be achieved by means of image sharpening algorithms is that finer structures in an image can be made to look more crisp. Take for example the coffee can image shown below, in which the upper lettering on the label in almost in focus, while the lower lettering has the softer appearance associated with de-focus.

The problem with this image is partially the fact that the blur is not uniform. Below are two regions enlarged:containing text from opposite ends of the blur spectrum.

Reducing blur, involves a concept known as image sharpening(which is different from removing motion blur, a much more challenging task). The easiest technique for image sharpening, and the one most often found in software such as Photoshop is known as unsharp masking. It is derived from analog photography, and basically works by subtracting a blurry version of the original image from the original image. It is by no means perfect, and is problematic in images where there is noise, as it tends to accentuate the noise, but it is simple.

Here I am using the “Unsharp Mask” filter from ImageJ. It subtracts a blurred copy of the image and rescales the image to obtain the same contrast of low frequency structures as in the input image. It works in the following manner:

  1. Obtain a Gaussian blurred image, by specifying a blur radius (in the example below the radius = 5).
  2. Filter the blurred image using a “Mask Weight, which determines the strength of filtering. A value from 0.1-0.9. (In the example below, the mask weight =0.4)
  3. Subtract the filtering image from the original image.
  4. Divide the resulting image by (1.0-mask weight) – 0.6 in the case of the example.
1. Original image; 2. Gaussian blurred image (radius=5); 3. Filtered image (multiplied by 0.4); 4. Subtracted image (original-filtered); 5. Final image (subtracted image / 0.6)

If we compare the resulting images, using an enlarged region, we find the unsharp masking filter has slightly improved the sharpness of the text in the image, but this may also be attributed to the slight enhancement in contrast. This part of the original image has less blur though, so let’s apply the filter to the second image.

The original image (left) vs. the filtered image (right)

Below is the result on the second portion of the image. There is next to no improvement in the sharpness of the image. So while it may be possible to slightly improve sharpness, where the picture is not badly blurred, excessive blur is impossible to “remove”. Improvements in acuity may be more to the slight contrast adjustments and how they are perceived by the eye.

Photographic blur you can’t get rid of

Photographs sometimes contain blur. Sometimes the blur is so bad that it can’t be removed, no matter the algorithm. Algorithms can’t solve everything, even those based on physics. Photography ultimately exists because of the existence of glass lenses – you can’t make any sort of camera without them. Lenses have aberrations (although lenses these days are pretty flawless) – some of these can be dealt with in-situ using corrective algorithms.

Some of this blur is attributable to vibration – no one has hands *that* steady, and tripods aren’t always convenient. Image stabilization, or vibration reduction has done a great job in retaining image sharpness. This is especially important in low-light situations where the photograph may require a longer exposure. The rule of thumb is that a camera should not be hand-held at shutter speeds slower than the equivalent focal length of the lens. So a 200mm lens should not be handheld at speeds slower than 1/200 sec.

Sometimes though, the screen on a digital camera doesn’t tell the full story either. The resolution may be too small to appreciate the sharpness present in the image – and a small amount of blur can reduce the quality of an image. Here is a photograph taken in a low light situation, which, with the wrong settings, resulted in a longer exposure time, and some blur.

Another instance relates to close-up, or macro photography, where the depth-of-field can be quiet shallow. Here is an  example of a close-up shot of the handle of a Norwegian mangle board. The central portion of the horse, near the saddle, is in focus, the parts to either side are not – and this form of blur is impossible to suppress. Ideally in order to have the entire handle in focus, one would have to use a technique known as focus stacking (available in some cameras).

Here is another example of a can where the writing at the top of the can is almost in focus, whereas the writing at the bottom is out-of-focus – due in part to the angle the shot was taken, and the shallow depth of field. It may be possible to sharpen the upper text, but reducing the blur at the bottom may be challenging.