Computational Imaging and Microscopy

This is an excellent talk, and takes one of the most complex subjects and breaks it down simply from the beginning.  

     14:38 in to the video. 

  It took be the better part of 25 years to learn the secret of zooming in to a license plate from an impossibly Zoomed image.  Something that when shown in Sci-Fi TV shows in the 70's and 80's - 90's...  I just assumed was Bullshit.  

 I eventually learned the secret from one of the top digital imaging experts that's I've known 25 years, and well after his retirement.  He implemented it on super top secret hardware for satellite imaging, when I was still in grade school.  It used the fact that every pixel in your image represents a sine cardinal from the whole image.  Also known as Sinc function it is the continuous inverse Fourier transform of a rectangular pulse and can be thought of a Gaussian modulated sine wave, although I am not really sure if these are the exact equivalent though I see it implemented in RF applications like this. 

In his case the Lens had to be extremely well understood, and in the end it generated convolution filters that were able to be ran quickly and efficiently. 

What is interesting is to see this generalized in to a generic computational imaging problem.  It may actually yield better results with more information, but most likely will be much more computation. 

Computational imaging involves the joint design of imaging system hardware and software, optimizing across the entire pipeline from acquisition to reconstruction. This talk will describe new methods for computational microscopy with coded illumination, based on a simple and inexpensive hardware modification of a commercial microscope. Traditionally, one must trade field-of-view for resolution; with our methods we can have both, resulting in Gigapixel-scale images with resolution beyond the diffraction limit of the system. Our reconstruction algorithms are based on large-scale nonlinear non-convex optimization procedures for phase retrieval. Laura Waller leads the Computational Imaging Lab, which develops new methods for optical imaging, with optics and computational algorithms designed jointly. She holds the Ted Van Duzer Endowed Professorship and is a Senior Fellow at the Berkeley Institute of Data Science (BIDS), with affiliations in Bioengineering and Applied Sciences & Technology. Laura was a Postdoctoral Researcher and Lecturer of Physics at Princeton University from 2010-2012 and received BS, MEng and PhD degrees from MIT in 2014, 2015 and 2010, respectively. She is a Moore Foundation Data-Driven Investigator, Bakar fellow, Distinguished Graduate Student Mentoring awardee, NSF CAREER awardee and Packard Fellow.

Using a single pixel to capture highres images.

CES2015

Bell Labs creates a lensless camera that's always in focus

http://www.theverge.com/2013/6/3/4391106/bell-labs-lensless-camera-technology-compressive-sensing

One day, cameras may be able to capture less data but produce images that look just as good as a traditional photo. Bell Labs is the latest to attempt such a feat, and it's doing so while eschewing another major camera standby, the lens. The laboratory has developed a single-pixel camera that only uses a series of transparent openings to capture its image, without any glass to direct the light. The system uses a technology called "compressive sensing," which is still in its early stages of study. The idea is that instead of a camera capturing a full image and then whittling the data down into a small, compressed file like a JPG, a camera could instead capture almost exactly what it needs, making capture times much quicker.

NO LENS, ALWAYS IN FOCUS

This isn't a reality just yet, however. Compressive sensing cameras build their final image by comparing the differences that come in through each aperture, and right now that takes too much time for them to shoot anything other than a still life. But by removing the lens, Bell Labs adds another impressive feature to its camera: its shots are always in focus. One always-in-focus camera, the Lytro, is already on the market, but Bell Labs sees its new tech as a practical way to shrink the size and cost of future cameras.

Bell Labs' device is built with "low cost, commercially available components," which primarily amount to a semi-transparent LCD panel, a one-megapixel imaging sensor, and a computer to connect it all to. The LCD panel was placed in front of the sensor, and light came in through white "openings" in the panel. The camera measured the data separately for red, green, and blue light, and used a computer to stitch together the final image. While the images don't demonstrate the finest image quality, they emphasize what compressive sensing is capable of. The books were captured using only a quarter of the camera's total imaging capabilities, and the soccer ball was captured using even less, just one-eighth.