Convolutional Neural Networks to Decode Images in a Wavefront Coding Imaging System
Year: 2024
Type: article
Abstract: Wavefront coding technique has been used to extend the depth of focus in an optical imaging system. An optical element called a phase mask allows coded images to be obtained since the point spread function remains almost invariant in an axial range. Subsequently, a computational technique is required to decode the acquired images. An optical-computational technique is proposed to use a phase mask for the coding stage and a convolutional neural network for the final restoration. Comparative results are made between cubic and trefoil profile phase masks and decoded using the traditional Wiener filter and a convolutional neural network. Image quality evaluation is done using the peak signal-to-noise ratio.
Authors: José Manuel Reyes Alfaro, Carina Toxqui Quitl, María Angélica Espejel Rivera, Alfonso Padilla Vivanco
Institution: Universidad Politécnica de Tulancingo
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404851801