DeepPD: Joint Phase and Object Estimation from Phase Diversity with Neural Calibration of a Deformable Mirror

• URL: http://arxiv.org/abs/2504.14157v1
• Date: Sat, 19 Apr 2025 03:04:21 GMT
• Title: DeepPD: Joint Phase and Object Estimation from Phase Diversity with Neural Calibration of a Deformable Mirror
• Authors: Magdalena C. Schneider, Courtney Johnson, Cedric Allier, Larissa Heinrich, Diane Adjavon, Joren Husic, Patrick La Rivière, Stephan Saalfeld, Hari Shroff,
• Abstract summary: We present DeepPD, a deep learning-based framework that combines neural representations of the object and wavefront with a learned model of the deformable mirror.<n>We demonstrate its performance on calibration targets and biological samples, including immunolabeled myosin in fixed PtK2 cells.
• Score: 0.14511217610551724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sample-induced aberrations and optical imperfections limit the resolution of fluorescence microscopy. Phase diversity is a powerful technique that leverages complementary phase information in sequentially acquired images with deliberately introduced aberrations--the phase diversities--to enable phase and object reconstruction and restore diffraction-limited resolution. These phase diversities are typically introduced into the optical path via a deformable mirror. Existing phase-diversity-based methods are limited to Zernike modes, require large numbers of diversity images, or depend on accurate mirror calibration--which are all suboptimal. We present DeepPD, a deep learning-based framework that combines neural representations of the object and wavefront with a learned model of the deformable mirror to jointly estimate both object and phase from only five images. DeepPD improves robustness and reconstruction quality over previous approaches, even under severe aberrations. We demonstrate its performance on calibration targets and biological samples, including immunolabeled myosin in fixed PtK2 cells.

Related papers

• Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models [58.98742597810023]
  Vision models have to behave in a robust way to disturbances such as noise or blur. This paper studies two datasets of blur corruptions, which we denote OpticsBench and LensCorruptions. Evaluations for image classification and object detection on ImageNet and MSCOCO show that for a variety of different pre-trained models, the performance on OpticsBench and LensCorruptions varies significantly.
  arXiv  Detail & Related papers  (2025-04-25T17:23:47Z)
• Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions [58.88917836512819]
  We propose a novel framework incorporating stereo depth estimation to enforce accurate geometric constraints. To mitigate the effects of poor lighting on stereo matching, we introduce Degradation Masking. Our method achieves state-of-the-art (SOTA) performance on the Multi-Spectral Stereo (MS2) dataset.
  arXiv  Detail & Related papers  (2024-11-06T03:30:46Z)
• Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology [43.237550839304774]
  We propose a speckle-conditioned diffusion model (SpecDiffusion) to reconstruct phase images directly from speckles captured at the detection side of a multi-core fiber (MCF)<n>Unlike conventional neural networks, SpecDiffusion employs iterative phase denoising steps for speckle-driven phase reconstruction.<n>SpecDiffusion provides high-fidelity phase reconstruction results and shows powerful generalization capacity for unseen objects.
  arXiv  Detail & Related papers  (2024-07-26T01:42:31Z)
• End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model [18.183342315517244]
  We propose a new hybrid ray-tracing and wave-propagation (ray-wave) model for accurate simulation of both optical aberrations and diffractive phase modulation. The proposed ray-wave model is fully differentiable, enabling gradient back-propagation for end-to-end co-design of refractive-diffractive lens optimization and the image reconstruction network.
  arXiv  Detail & Related papers  (2024-06-02T18:48:22Z)
• Spatial super-resolution in nanosensing with blinking emitters [79.16635054977068]
  We propose a method of spatial resolution enhancement in metrology with blinking fluorescent nanosensors. We believe that blinking fluorescent sensing agents being complemented with the developed image analysis technique could be utilized routinely in the life science sector.
  arXiv  Detail & Related papers  (2024-02-27T10:38:05Z)
• Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction [65.5397271106534]
  A single neural network is difficult to handle all exposure problems. In particular, convolutions hinder the ability to restore faithful color or details on extremely over-/under- exposed regions. We propose a Macro-Micro-Hierarchical transformer, which consists of a macro attention to capture long-range dependencies, a micro attention to extract local features, and a hierarchical structure for coarse-to-fine correction.
  arXiv  Detail & Related papers  (2023-09-02T09:07:36Z)
• Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers [2.236663830879273]
  spatial resolution of images of living samples obtained by fluorescence microscopes is physically limited due to the diffraction of visible light. Several deconvolution and super-resolution techniques have been proposed to overcome this limitation.
  arXiv  Detail & Related papers  (2023-03-20T15:43:52Z)
• Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution [51.274657266928315]
  We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
  arXiv  Detail & Related papers  (2021-04-07T12:00:38Z)
• SIR: Self-supervised Image Rectification via Seeing the Same Scene from Multiple Different Lenses [82.56853587380168]
  We propose a novel self-supervised image rectification (SIR) method based on an important insight that the rectified results of distorted images of the same scene from different lens should be the same. We leverage a differentiable warping module to generate the rectified images and re-distorted images from the distortion parameters. Our method achieves comparable or even better performance than the supervised baseline method and representative state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-30T08:23:25Z)
• Spatially-Variant CNN-based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy [6.09170287691728]
  We present a method that improves the resolution of light microscopy images of thin, yet non-flat objects. We estimate the parameters of a spatially-variant Point-Spread function (PSF) model using a Convolutional Neural Network (CNN) Our method recovers PSF parameters from the image itself with up to a squared Pearson correlation coefficient of 0.99 in ideal conditions.
  arXiv  Detail & Related papers  (2020-10-08T14:20:16Z)
• Optimization of collection optics for maximum fidelity in entangled photon sources [0.0]
  decoherence sources for entangled photons created by spontaneous parametric down conversion phenomenon is studied. The phase and spatial distinguishability of photon pairs from crystals reduce the maximum achievable entanglement fidelity. A realistic scenario is numerically modelled, where the photon pairs with nonzero emission angle gather a phase difference.
  arXiv  Detail & Related papers  (2020-07-14T00:48:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.