Researchers developed a way to measure information in imaging systems using only noisy sensor data.
Traditional metrics like resolution and signal-to-noise ratio assess quality separately. This makes it hard to compare systems that trade off between different factors.
Their method uses mutual information to quantify how much measurements reduce uncertainty about an object.
This single number captures the combined effect of resolution, noise, and sampling together.
They avoided previous problems by estimating information directly from measurements without explicit object models.
The framework estimates total measurement variation and subtracts known noise-only variation.
Researchers tested three probabilistic models: a stationary Gaussian process, a full Gaussian model, and an autoregressive PixelCNN.
Information estimates correctly predicted performance across four distinct imaging domains.
Color photography tests compared traditional Bayer patterns against random and learned filter arrangements.
Radio astronomy evaluations selected optimal telescope locations without expensive image reconstruction.
Lensless imaging designs were ranked by information content despite lacking visual resemblance to scenes.
Microscopy experiments correlated information estimates with neural network accuracy on protein expression.
High information consistently produced better results on downstream tasks in all tested applications.
Source: http://bair.berkeley.edu/blog/2026/01/10/information-driven-imaging/
