Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A New Deep Learning Method for Image Deblurring in Optical Microscopic Systems (1910.03928v1)

Published 8 Oct 2019 in eess.IV

Abstract: Deconvolution is the most commonly used image processing method to remove the blur caused by the point-spread-function (PSF) in optical imaging systems. While this method has been successful in deblurring, it suffers from several disadvantages including being slow, since it takes many iterations, suboptimal, in cases where experimental operator chosen to represent PSF is not optimal. In this paper, we are proposing a deep-learning-based deblurring method applicable to optical microscopic imaging systems. We tested the proposed method in database data, simulated data, and experimental data (include 2D optical microscopic data and 3D photoacoustic microscopic data), all of which showed much improved deblurred results compared to deconvolution. To quantify the improved performance, we compared our results against several deconvolution methods. Our results are better than conventional techniques and do not require multiple iterations or pre-determined experimental operator. Our method has the advantages of simple operation, short time to compute, good deblur results and wide application in all types of optical microscopic imaging systems. The deep learning approach opens up a new path for deblurring and can be applied in various biomedical imaging fields.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (11)
  1. Huangxuan Zhao (10 papers)
  2. Ziwen Ke (14 papers)
  3. Ningbo Chen (1 paper)
  4. Ke Li (723 papers)
  5. Lidai Wang (3 papers)
  6. Xiaojing Gong (2 papers)
  7. Wei Zheng (138 papers)
  8. Liang Song (60 papers)
  9. Zhicheng Liu (41 papers)
  10. Dong Liang (154 papers)
  11. Chengbo Liu (2 papers)
Citations (42)

Summary

We haven't generated a summary for this paper yet.