Photoshop Engineer Unblurs Motion & Restores Focus

Here is a quickie, Wild Ducks. File this one under "Wow!"

This demonstration by an Adobe PhotoShop developer forces me to rethink my understanding of focus and information recovery.

[caption id="attachment_770" align="alignright" width="294"] Deconvolution restores information, but only if captured in original image and obfuscated via a reversible & non-lossy process. The filter proves that motion blur meets the criteria. It is not indicative of missing information![/caption]

Until now, I thought that motion blur (example #1 in the video) and focus (example #2) were evidence of lost information—and therefore, they could not be overcome. That is, if a camera is out of focus or moving in relation to its subject, it is part way along the path to a complete loss of picture information (for example, a camera that is totally unfocussed or moving in a complete circle with the shutter open. In the extreme case, film is exposed to unfocussed light...no useful information). But this video proves that there exist algorithms that can make reasonable measurements and assumptions about the original scene and then recover sharpness and lost information.

Listen to the audience reaction at these times in the video:  1:17 & 3:33. The process is startling because it appears to recover information and not just perceived sharpness. Click for close ups of before-&-after that wow'd the audience  [Plaza]   [Cruise poster]

An existing 3rd party plugin, Focus Magic, may do the same thing. It is pitched to forensic investigators. (Note to Wild Ducks: Thwarting forensics is a noble calling). Focus Magic touts startling before-&-after photos of a blurry license plate which becomes easily readable after processing. Their web site highlights the restoration of actual sharpness through a process of deconvolution* as opposed to simply enhancing perceived sharpness by applying faux features such as unsharp mask or edge acutance. It is not clear if the two projects use the same underlying technique.

Implications for File Compression (e.g. JPEG)

Here’s something for armchair mathematicians to ponder. If we compare two compressed files: An image with sharp focus and an identical image that is unfocused but still recoverable, we see that the file size of the unfocused image is considerably smaller. In the past, we explained this based on the assumption that the unfocused image contains less information, as if we had resampled the original image at a lower resolution.

But if we our compressed file size relates to the information content of the uncompressed image, then how do we explain the smaller file size of an unfocused image? If detail in the unfocused image is recoverable, than we should be able to boost file compression by intentionally unfocusing images and then restoring focus during decompression. For example, suppose that we were to add this transformation to the JPEG compression formula. Could we compress images into significantly smaller files? In theory the same should be true for lossless compression methods such as TIF/CCITT.

* Deconvolution is a field of mathematics & signal processing that refers to the removal of noise or distortion and revealing meaningful information hidden within a polluted file or signal. What is surprising about the PhotoShop demonstration (and perhaps the process used by Focus Magic) is that there exists a deconvolution process for information that I had assumed was never captured during the original recording process.