Deeply coded aperture for lensless imaging
Ryoichi Horisaki, Yuka Okamoto, Jun Tanida
Abstract
In this Letter, we present a method for jointly designing a coded aperture and a convolutional neural network for reconstructing an object from a single-shot lensless measurement. The coded aperture and the reconstruction network are connected with a deep learning framework in which the coded aperture is placed as a first convolutional layer. Our co-optimization method was experimentally demonstrated with a fully convolutional network, and its performance was compared to a coded aperture with a modified uniformly redundant array.
Topics & Concepts
Coded apertureComputer scienceOpticsConvolutional neural networkAperture (computer memory)Artificial intelligenceIterative reconstructionComputer visionPhysicsDetectorAcousticsAdvanced Optical Sensing TechnologiesOptical measurement and interference techniquesDigital Holography and Microscopy