Efficient target recognition in ghost imaging via optimized 1D convolutional networks at a low sampling ratio

TY - JOUR

T1 - Efficient target recognition in ghost imaging via optimized 1D convolutional networks at a low sampling ratio

AU - Abbas, Ayesha

AU - Cao, Jie

AU - Rehman, Adeel

AU - Idrees, Bushra Sana

AU - Sabir, Shahwal

N1 - Publisher Copyright: © 2026 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

PY - 2026/3/1

Y1 - 2026/3/1

N2 - The efficient use of a convolutional neural network (CNN) for object recognition in both conventional imaging and computational ghost imaging (CGI) modalities is demonstrated in this work. The model, which is trained on the Fashion-MNIST dataset, exhibits 92.91% accuracy in image-based classification; class uniqueness has a strong correlation with the performance. More importantly, the model achieves an enhanced accuracy of 97.55% in an image-free CGI framework using only compressed bucket measurements at an 80% sampling ratio. This demonstrates how it can reliably extract features directly from highly subsampled, one-dimensional data, allowing for effective, low-bandwidth optical sensing. We also investigate how resilient the architecture is to noise, and we find that a noise-augmented training approach can significantly reduce performance deterioration in high-noise situations. Thus, the suggested CNN architecture bridges the gap between conventional image processing and applied computational optics by offering a flexible and useful solution for quick, noise-resilient object recognition.

AB - The efficient use of a convolutional neural network (CNN) for object recognition in both conventional imaging and computational ghost imaging (CGI) modalities is demonstrated in this work. The model, which is trained on the Fashion-MNIST dataset, exhibits 92.91% accuracy in image-based classification; class uniqueness has a strong correlation with the performance. More importantly, the model achieves an enhanced accuracy of 97.55% in an image-free CGI framework using only compressed bucket measurements at an 80% sampling ratio. This demonstrates how it can reliably extract features directly from highly subsampled, one-dimensional data, allowing for effective, low-bandwidth optical sensing. We also investigate how resilient the architecture is to noise, and we find that a noise-augmented training approach can significantly reduce performance deterioration in high-noise situations. Thus, the suggested CNN architecture bridges the gap between conventional image processing and applied computational optics by offering a flexible and useful solution for quick, noise-resilient object recognition.

UR - https://www.scopus.com/pages/publications/105031584911

U2 - 10.1364/AO.587775

DO - 10.1364/AO.587775

M3 - Article

AN - SCOPUS:105031584911

SN - 1559-128X

VL - 65

SP - 6454

EP - 7662

JO - Applied Optics

JF - Applied Optics

IS - 7

ER -