Learning Phase Diversity Solves Ill-Posed Inverse Imaging Problems with Deep Networks

Ill-posed inverse problems frequently plague imaging techniques, hindering the accurate reconstruction of images from raw data, but researchers are now demonstrating a pathway to improved solutions through learned data diversity. Jasleen Birdi, Tamal Majumder, and Debanjan Halder, all from the Indian Institute of Technology Delhi, alongside Muskan Kularia and Kedar Khare, present a novel method that leverages the inherent correlations within phase-diverse measurements to generate synthetic data. This physics-informed data augmentation scheme trains a network to create pseudo-data from existing measurements, effectively increasing data diversity without requiring additional hardware or complex setups. The team validates this approach for both incoherent and coherent imaging, demonstrating that combining real and augmented data significantly enhances reconstruction quality and promises leaner, high-fidelity imaging systems for a wide range of applications.

Deep Learning Enhances Phase Retrieval Imaging

This research details a novel approach to computational imaging using deep learning, specifically focusing on enhancing image quality and resolution. The team explored deep learning techniques, particularly U-Nets, to improve image reconstruction in challenging scenarios, integrating concepts like phase retrieval, diversity, and Wiener filtering within a deep learning framework. A U-Net serves as the core deep learning model, addressing the ill-posed problem of phase retrieval crucial for reconstructing images from intensity measurements common in many imaging modalities. The model was trained and tested on various datasets, demonstrating significant improvements in image quality and resolution compared to traditional methods. The proposed approach addresses the twin-image problem, proposing a method to avoid stagnation during iterative phase retrieval, leading to more robust and accurate reconstructions. This work contributes to the growing field of computational imaging, offering a promising pathway for developing advanced imaging systems with improved performance and potential applications in microscopy, medical imaging, astronomy, and security.

Predicting Phase Diversity with Deep Networks

Scientists developed a novel data augmentation scheme to improve image reconstruction quality in both incoherent and coherent imaging systems. The study pioneers a method that leverages implicit local correlations within phase-diverse measurements of an object, learning these relationships through deep neural networks. Rather than relying on complex hardware, the team engineered a system that generates synthetic data from a single measurement, effectively expanding the available data without additional sensors. The core of the method involves training a deep network to predict phase-diverse data, creating additional views of the object from a single measurement. This augmented dataset dramatically reduces the ill-posedness of the reconstruction problem, leading to stable and high-quality image solutions less sensitive to parameter tuning. Researchers validated this approach utilizing vortex phase as a diversity mechanism, moving beyond conventional iterative reconstruction algorithms by introducing a data-driven approach to address the fundamental challenges of inverse imaging.

Phase Diversity Improves Image Reconstruction Quality

Scientists have developed a novel approach to improve image reconstruction in both incoherent and coherent imaging systems, addressing the inherent challenges of inverse problems. The work demonstrates that by leveraging implicit local correlations within phase-diverse measurements, high-quality images can be reconstructed with simpler algorithms. Researchers observed that phase-diverse data, generated using a trained network from a single measurement, effectively reduces the ambiguity in image reconstruction. The team validated this approach by generating pseudo-data, augmenting true data, and observing significant improvements in reconstruction quality. This augmented data allows for stable and high-quality image solutions without the need for complex parameter tuning typically required by traditional optimization methods. This breakthrough delivers a means to create leaner, high-fidelity computational imaging systems applicable across a broad range of applications, including microscopy and computational imaging.

Learned Correlations Enhance Image Reconstruction

This work introduces a new framework for improving image reconstruction in both incoherent and coherent imaging systems. Researchers successfully demonstrated a method for generating synthetic, yet physically plausible, data to supplement single measurements, thereby enhancing the robustness of image reconstruction without requiring changes to imaging hardware. The core of this achievement lies in recognizing and learning the implicit spatial correlation present in paired measurements obtained from the same object. By training a network to predict one measurement from another, the team created augmented data that, when combined with real measurements, significantly reduces the challenges associated with ill-posed inverse problems. This approach allows for the use of simpler reconstruction algorithms while maintaining high image quality, opening possibilities for streamlined imaging systems across a range of applications.