Traditional cameras rely on bulky glass lenses to focus light into human-interpretable images. However, a new generation of “lensless” imagers is stripping away the glass, replacing it with thin optical masks and sophisticated algorithms. While these systems promise to make cameras thinner and more versatile, designing the perfect mask has long been a challenge of trial and error.
Researchers at UC Berkeley’s Department of Electrical Engineering and Computer Sciences (EECS) have developed a fundamentally new framework to solve this problem. In a study published in Optica, the team describes a method that applies principles from information theory to optimize these designs. By doing so, they have moved away from judging a camera by how “good” its raw measurements look, focusing instead on how much information is actually being captured.
Shifting the Paradigm: From Aesthetics to Information
In a lensless system, a thin mask patterns light across an image sensor. This data is then processed by a reconstruction algorithm to produce a final image. Historically, researchers evaluated these designs based on the visual quality of the reconstruction.
The Berkeley team, led by Professor Laura Waller, argues for a different approach based on mutual information. This concept quantifies exactly how much information the sensor’s measurement contains about the original scene.
