Differentiable Microscopy for Content and Task Aware Compressive Fluorescence Imaging

• URL: http://arxiv.org/abs/2203.14945v1
• Date: Mon, 28 Mar 2022 17:53:10 GMT
• Title: Differentiable Microscopy for Content and Task Aware Compressive Fluorescence Imaging
• Authors: Udith Haputhanthri, Andrew Seeber, Dushan Wadduwage
• Abstract summary: Trade-off between throughput and image quality is an inherent challenge in microscopy. Deep Learning based methods have achieved greater success in compression and image quality. We propose differentiable compressive fluorescence microscopy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The trade-off between throughput and image quality is an inherent challenge in microscopy. To improve throughput, compressive imaging under-samples image signals; the images are then computationally reconstructed by solving a regularized inverse problem. Compared to traditional regularizers, Deep Learning based methods have achieved greater success in compression and image quality. However, the information loss in the acquisition process sets the compression bounds. Further improvement in compression, without compromising the reconstruction quality is thus a challenge. In this work, we propose differentiable compressive fluorescence microscopy ($\partial \mu$) which includes a realistic generalizable forward model with learnable-physical parameters (e.g. illumination patterns), and a novel physics-inspired inverse model. The cascaded model is end-to-end differentiable and can learn optimal compressive sampling schemes through training data. With our model, we performed thousands of numerical experiments on various compressive microscope configurations. Our results suggest that learned sampling outperforms widely used traditional compressive sampling schemes at higher compressions ($\times 100- 1000$) in terms of reconstruction quality. We further utilize our framework for Task Aware Compression. The experimental results show superior performance on segmentation tasks even at extremely high compression ($\times 4096$).

Related papers

• Transferable Learned Image Compression-Resistant Adversarial Perturbations [66.46470251521947]
  Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks. We introduce a new pipeline that targets image classification models that utilize learned image compressors as pre-processing modules.
  arXiv  Detail & Related papers  (2024-01-06T03:03:28Z)
• Deep learning based Image Compression for Microscopy Images: An Empirical Study [3.915183869199319]
  This study analyzes classic and deep learning based image compression methods, and their impact on deep learning based image processing models. To compress images in such a wanted way, multiple classical lossy image compression techniques are compared to several AI-based compression models. We found that AI-based compression techniques largely outperform the classic ones and will minimally affect the downstream label-free task in 2D cases.
  arXiv  Detail & Related papers  (2023-11-02T16:00:32Z)
• Machine Perception-Driven Image Compression: A Layered Generative Approach [32.23554195427311]
  layered generative image compression model is proposed to achieve high human vision-oriented image reconstructed quality. Task-agnostic learning-based compression model is proposed, which effectively supports various compressed domain-based analytical tasks. Joint optimization schedule is adopted to acquire best balance point among compression ratio, reconstructed image quality, and downstream perception performance.
  arXiv  Detail & Related papers  (2023-04-14T02:12:38Z)
• High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation [24.379052026260034]
  We propose the Invertible Activation Transformation (IAT) module to tackle the issue of high-fidelity fine variable-rate image compression. IAT and QLevel together give the image compression model the ability of fine variable-rate control while better maintaining the image fidelity. Our method outperforms the state-of-the-art variable-rate image compression method by a large margin, especially after multiple re-encodings.
  arXiv  Detail & Related papers  (2022-09-12T07:14:07Z)
• Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training [90.76576712433595]
  Applying lossy compression on images processed by deep neural networks can lead to significant accuracy degradation. Inspired by the curriculum learning paradigm, we present a novel training approach called curriculum pre-training (CPT) for crowd counting on compressed images.
  arXiv  Detail & Related papers  (2022-08-15T08:43:21Z)
• Estimating the Resize Parameter in End-to-end Learned Image Compression [50.20567320015102]
  We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our results show that our new resizing parameter estimation framework can provide Bjontegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines.
  arXiv  Detail & Related papers  (2022-04-26T01:35:02Z)
• Learning Scalable $\ell_\infty$-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression [118.89112502350177]
  We propose a novel framework for learning $ell_infty$-constrained near-lossless image compression. We derive the probability model of the quantized residual by quantizing the learned probability model of the original residual.
  arXiv  Detail & Related papers  (2021-03-31T11:53:36Z)
• Analyzing and Mitigating JPEG Compression Defects in Deep Learning [69.04777875711646]
  We present a unified study of the effects of JPEG compression on a range of common tasks and datasets. We show that there is a significant penalty on common performance metrics for high compression.
  arXiv  Detail & Related papers  (2020-11-17T20:32:57Z)
• Learning End-to-End Lossy Image Compression: A Benchmark [90.35363142246806]
  We first conduct a comprehensive literature survey of learned image compression methods. We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes. By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance.
  arXiv  Detail & Related papers  (2020-02-10T13:13:43Z)

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.