Login / Signup

Postrecording Pixel-Reconstruction Approach for Correcting the Lateral Drifts in Surface Plasmon Resonance Microscope.

Jia GaoXiang WoYongjie WangMinghe LiChunyuan ZhouWei Wang
Published in: Analytical chemistry (2019)
Surface plasmon resonance microscope (SPRM) sample stage inevitably suffers from lateral drifts as a result of many environmental factors including thermal fluctuation, mechanical vibration, and relaxation. It places great obstacles to time-lapsed imaging and measurements that need high spatial resolution or long recording time. Existing solutions often require experimental efforts such as the addition of optical markers together with piezoelectric stage-based active feedback configurations. Herein, we propose an all-digital, postrecording image-processing method to remove the lateral drift in a series of time-lapsed SPRM images. The method first calculates the value of lateral drift at subpixel accuracy by combining image cross-correlation analysis and superlocalization strategy. It subsequently reconstructed the drift-free image sequences in a pixel-by-pixel and frame-by-frame manner, according to the linear decomposition and reconstruction principle. This method purely relies on image processing, and it does not require any experimental efforts or hardware. In addition to SPRM, we further demonstrated the applicability of the present method in other types of optical imaging techniques including bright-field transmission microscope and dark-field scattering microscope.
Keyphrases
  • deep learning
  • high resolution
  • minimally invasive
  • convolutional neural network
  • high speed
  • machine learning
  • quality improvement
  • fluorescence imaging
  • genetic diversity
  • neural network