What makes us experience beauty?

I have spent over two decades writing algorithms for image processing, however I have never really created anything uber fulfilling . Why? Because it is hard to create generic filters, especially for tasks such as image beautification. In many ways improving the aesthetic appeal of photographs involves modifying the content on an image in more non natural ways. It doesn’t matter how AI-ish an algorithm is, it cannot fathom what the concept of aesthetic appeal is.  A photograph one person may find pleasing may be boring to others. Just like a blank canvas is considered art to some, but not to others. No amount of mathematical manipulation will lead to a algorithmic panacea of aesthetics. We can modify the white balance and play with curves, indeed we can make 1001 changes to a photograph, but the final outcome will be perceived differently by different people.

After spending years researching image processing algorithms, and designing some of my own, it wasn’t until I decided to take the art of acquiring images to a greater depth that I realized algorithms are all good and well, but there is likely little need for the plethora of algorithms created every year. Once you pick up a camera, and start playing with different lenses, and different camera settings, you begin to realize that part of the nuance any photograph is its natural aesthetic appeal. Sure, there are things that can be modified to improve aesthetic appeal, such as contrast enhancement or improving the sharpness, but images also contain unfocused regions that contribute to their beauty.

If you approach image processing purely from a mathematical (or algorithmic) viewpoint, what you are trying to achieve is some sort of utopia of aesthetics. But this is almost impossible, largely because every photography is unique.  It is possible to improve the acuity of objects in an image using techniques such as unsharp masking, but it is impossible to resurrect a blurred image – but maybe that’s the point. One could create an fantastic filter that sharpens an image beautifully, but with the sharpness of modern lenses, that may not be practical. Consider this example of a photograph taken in Montreal. The image has good definition of colour, and has a histogram which is fairly uniform. There isn’t a lot that can be done to this image, because it truly does represent the scene as it exists in real life. If I had taken this photo on my iPhone, I would be tempted to post it on Instagram, and add a filter… which might make it more interesting, but maybe only from the perspective of boosting colour.

Here is the same image with only the colour saturation boosted (by ×1.6). Have its visual aesthetics been improved? Probably. Our visual system would say it is improved, but that is largely because our eyes are tailored to interpret colour.

If you take a step back from the abyss of algorithmically driven aesthetics, you begin to realize that too few individuals in the image processing community have taken the time to really understand the qualities of an image. Each photograph is unique, and so the idea of generic image processing techniques is highly flawed. Generic techniques work sufficiently well in machine vision applications where the lighting is uniform, and the task is also uniform, e.g. inspection of rice grains, or identification of burnt potato chips. No aesthetics are needed, just the ability to isolate an object and analyze it for whatever quality is needed. It’s one of the reasons unsharp masking has always been popular. Alternative algorithms for image sharpening really don’t work much better. And modern lenses are sharp, in fact many people would be more likely to add blur than take it away.