Diffusion Model Improves Phase Retrieval from Intensity-Only Measurements

Recovering detailed information from limited data presents a significant challenge in many scientific fields, and phase retrieval – the process of reconstructing a signal from measurements of its intensity alone – is central to advances in imaging, holography, and microscopy. Mehmet Onurcan Kaya and Figen S. Oktem, along with their colleagues, address this challenge by introducing a new phase retrieval method called I2I-PR, which leverages the power of image-to-image diffusion models. This innovative approach overcomes the limitations of existing techniques, which often struggle with noisy data and require careful initialisation, by combining a robust initial estimate with iterative refinement using a learned image pipeline. The results demonstrate substantial improvements in both the speed and quality of reconstruction, offering a potentially transformative tool for a wide range of scientific applications where detailed image recovery is crucial.

Recovering Lost Information: A New Approach to Phase Retrieval

Many scientific and engineering fields rely on the ability to reconstruct a signal or image from incomplete information. A particularly challenging problem, known as phase retrieval, arises when only the intensity of a signal is measured, losing crucial information about its wave-like properties – its ‘phase’. This is akin to determining the shape of an object by only knowing its brightness, without any information about its depth or texture. Traditional methods for solving this problem struggle with noise and require good initial guesses to produce accurate results. While recent advances have explored the use of deep learning, these methods often require extensive training and can perform poorly when applied to data that differs from what they were trained on. To address these limitations, researchers have developed a new approach that leverages the power of image-to-image diffusion models – a type of deep learning – but with a crucial difference. Instead of starting from random noise, the method begins with a rough initial estimate generated by established algorithms. This ‘warm start’ allows the diffusion model to focus on refining an existing solution, rather than building one from scratch, significantly improving both the speed and robustness of the reconstruction. The team further enhanced their approach with a novel accelerated error reduction algorithm for generating the initial estimate and a technique for combining multiple reconstructions to improve image quality. The results demonstrate that this hybrid method outperforms both classical and contemporary techniques, offering a significant step forward in inverse problem solving and paving the way for more accurate and reliable image reconstruction in diverse scientific disciplines.

Diffusion Improves Image Reconstruction

Quality Researchers have now developed a new methodology that significantly improves both the speed and quality of phase retrieval by drawing inspiration from recent advances in image processing. This innovative approach effectively leverages initial estimates to accelerate the training process and focus computational resources on refining the resulting images. The core of the method lies in an image-to-image diffusion model, a type of deep learning framework that learns to transform noisy or incomplete images into clear, detailed ones. The researchers have carefully unrolled the steps of traditional iterative algorithms and translated them into a series of trainable layers within the diffusion model, allowing the system to learn how to refine the initial estimate best. Furthermore, the team incorporated techniques to ensure the reconstructed image consistently matches the original measurements, maintaining accuracy throughout the refinement process. The results demonstrate superior performance in perceptual and distortion metrics, indicating an advancement in overall image quality. The team highlights the potential for this approach to benefit various fields reliant on phase retrieval, such as imaging, microscopy, and crystallography, by providing a more efficient and accurate means of reconstructing signals from limited data.

Inversion by Direct Iteration for Phase Retrieval

Reconstructing detailed images from limited information is a significant challenge in many scientific fields, including microscopy and crystallography. A technique called phase retrieval aims to reconstruct an image when only its intensity–brightness is measured, losing crucial information about the image’s wave-like properties. Traditional methods struggle with noise and require carefully chosen starting points to produce accurate results. Researchers have now developed a new approach that combines the strengths of two established algorithms – Hybrid Input-Output and Error Reduction – with a novel acceleration mechanism to generate a robust initial estimate of the image. This ‘warm-start’ dramatically improves efficiency, allowing the system to refine an existing approximation rather than building an image from scratch. By combining a strong initial estimate with a learned refinement pipeline and measurement consistency, this method demonstrably outperforms both classical and contemporary phase retrieval techniques, offering a powerful new tool for visualizing hidden details in a wide range of scientific applications.