“Genius is 1% inspiration and 99% perspiration.”

So it went with Nesota.   I considered many ideas, including:

• Group travel planning/coordination service, born out of my own frustration
• Consumer-grade thermal imaging camera, prompted by stories of volunteer firefighters borrowing the departments’ thermal cameras for use as hunting aids
• Group gift service, where money for a gift from a group could be collected and the gift selected
• Coaching hub, where students could find coaches, coaches could find students, and payment could be exchanged in a formal manner (”YouCoach.Me”)
• Broad automotive enthusiast site, in the style of BonnevilleClub.com (my successful niche site for Pontiac Bonneville owners, now sold)
• Photography TV channel or show, showing how to achieve various ends through technique (kind of like Good Eats meets The Shot)
• Service for pestering people to stop procrastinating and start working, targeted at entrepreneurs who can’t seem to get their projects going

In the months and years since my initial thoughts, some of these have come to be (for example, StickK partially fulfills the nagging-service use case), and others remain frustratingly absent (like the consumer-grade thermal camera, though it seems to be a ripe opportunity for Redshift Systems).  Part of the challenge in selecting an idea is having the perseverance to stick with a single idea instead of running off with the idea-of-the-week, each of which is “surely easier” and “certainly more profitable” than the original idea under development.

After a bunch of false starts (anybody want to buy the domain YouCoach.Me?), I settled on a computational photography idea that grew out of a discussion with a good friend.  What, specifically?  Well, if a photographer or the camera makes a mistake with the exposure setting, the white balance, or the framing, all of those problems can be corrected rather simply in post-production.  However, if a focusing error is made, the photographer has few good options.  Sure, he can hit the picture with “unsharp mask” and its brethren, but those filters serve only to increase the acutance of the image.  They don’t fix the underlying focus problem.  What to do?

It turns out that there’s a better way.  A way that’s been used to a limited extent in astronomy and microscopy for years.  A way that presents an exceptionally difficult technical challenge to the implementer.  A way that’s ripe for commercialization.

Imagine: if your camera produces a blurry photo because of a focusing error or camera movement, this technique can recover the latent sharp image and save the day.  Such is the beauty of the current idea.

The challenge now is the implementation.