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At times our optical inspection system gets out of focus which results in nonsensical measurements. I've been tasked to develop an 'out of focus' detector which will be used to drive the Z axis of the camera system. The images available to me are bmp.
I'm looking for approaches and algorithms to investigate. For instance, should I be isolating features and measuring conformance or could edge detection be used?
This is the in focus image: 
And this is the out of focus image: 
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The easiest way to detect if an image is blurry or not is to look at the strength of the high frequency content. This can be done with a simple gradient of gaussian filter or a laplacian filter.
The aperture of your lens will determine how much light is let in and as a consequence, how well you'll be able to focus. Photo by: 'Unsplash'. Practically speaking, you can check whether a picture is in focus or not by its sharpness. The sharper that it looks, the more it will be in focus.
An image, or image point or region, is in focus if light from object points is converged almost as much as possible in the image, and out of focus if light is not well converged. The border between these is sometimes defined using a "circle of confusion" criterion.
The key is that in-focus image has much more strong gradients and sharp features. So what I suggest is to apply a Gaussian Laplace filter and then look at the distribution of pixel values of the result. The plot below shows the application of this idea to your images, where black refers to the out of focus image, and red to the one in focus. The in-focus one has much more high values (because the image has more sharp gradients).
When you have the histograms, you can distinguish one from another by comparing e.g. 90%th percentiles of the distributions (which is sensitive to tails). For the out of focus image it is 7 and for the in-focus image it is 13.6 (so twice the difference).
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A quick and dirty version of the contrast algorithm is to sum the differences between adjacent pixels - higher sum is more contrast.
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