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Implicit Neural Image Field for Biological Microscopy Image Compression

arXiv 2405.19012 ↗First posted 29 May 2024cs.AIcited by 0

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Abstract (arXiv)

The rapid pace of innovation in biological microscopy imaging has led to large images, putting pressure on data storage and impeding efficient sharing, management, and visualization. This necessitates the development of efficient compression solutions. Traditional CODEC methods struggle to adapt to the diverse bioimaging data and often suffer from sub-optimal compression. In this study, we propose an adaptive compression workflow based on Implicit Neural Representation (INR). This approach permits application-specific compression objectives, capable of compressing images of any shape and arbitrary pixel-wise decompression. We demonstrated on a wide range of microscopy images from real applications that our workflow not only achieved high, controllable compression ratios (e.g., 512x) but also preserved detailed information critical for downstream analysis.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 49 references that are in the map. First posted on arXiv 29 May 2024.

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References · 13

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  3. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  4. 2016Learning to learn by gradient descent by gradient descentcited by 616
  5. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 550
  6. 2021NeRV: Neural Representations for Videoscited by 255
  7. 2023Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessmentcited by 227
  8. 2021Modulated Periodic Activations for Generalizable Local Functional Representationscited by 140
  9. 2020Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselvescited by 57
  10. 2022VeLO: Training Versatile Learned Optimizers by Scaling Upcited by 57
  11. 2020Using a thousand optimization tasks to learn hyperparameter search strategiescited by 29
  12. 2023Siamese SIREN: Audio Compression with Implicit Neural Representationscited by 11
1 more
  1. 2023MoEC: Mixture of Experts Implicit Neural Compressioncited by 4

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