Scalable Solutions to Image Abnormality Detection and Restoration using Limited Contextual Information

• Author / Creator

  Mukherjee,Subhayan

• Detecting and interpreting image abnormalities and restoring images are essential to many processing pipelines in diverse fields. Challenges involved include randomness and unstructured nature of image artefacts (from signal processing perspective) and performance constraints imposed by resource-constrained systems (computational perspective). This thesis studies three such scenarios involving different sensor modalities, and proposes novel approaches to address such challenges using computer vision and machine learning techniques. The three scenarios are: GPU-friendly debanding for mobile HDR, MRI abnormality detection, and InSAR signal recovery for WAM. Below, we summarize each scenario, and outline the unique challenges involved in solving them.

  In the first scenario, in High-Dynamic-Range (HDR) imaging domain, we propose how to perceptually eliminate quantization artefacts resulting from dynamic range conversion, without distorting image contents, by adding noise patterns in the mobile Graphics Processing Unit (GPU) computing environment with limited computational resources. Traditional filtering methods are computationally non-ideal due to limited access to neighborhood information. We introduce a pixel based solution which does not rely on any neighborhood information. We impose a real-world transmission scenario where the receiving end cannot access the un-quantized (original) image. Most existing methods assume access to the original image, and this makes the image restoration problem easier to address. Our challenge is designing noise patterns that are perceptually pleasant, i.e. blended into the image content naturally based on the intensity profile and dynamic range conversion characteristics of the image.

  In the second scenario, we apply our methods to medical imaging: Magnetic Resonance (MR) images of preterm infant brains. We propose abnormality detection without using tissue priors (atlas) from a single acquisition sequence (T1-weighted). The rate of preterm births is increasing worldwide at an alarming rate. Preterm infant brains are at extremely high risk of developing abnormalities, which deter neuro-development. Traditional segmentation-based lesion detection methods rely on brain atlases to guide segmentation. For rapidly evolving preterm infant brains, such reliable atlases are unavailable, and low signal-to-noise ratio of small preterm infant brains complicates the restoration of tissue intensities. This motivates our solution design using more generic structural assumptions and heuristics for atlas-free WMI detection.

  In the third scenario, for Wide Area Monitoring (WAM) via ground movement prediction from noisy interferometric images, e.g. nation-wide monitoring for earthquake prediction or landslide prediction at mining sites, we propose image detail recovery through unsupervised learning-based filtering and filter output confidence prediction due to unavailability of clean training data. Filtering and confidence estimation are crucial steps in interferometric image processing pipelines. Challenges arise due to atmospheric factors, e.g. moisture, corrupting the images during acquisition. Also, corresponding pixels in time series images get decorrelated due to other undesired movements on the ground, e.g. moving vehicles on roads, vegetation and flowing water. Intermediate results of the filtering and confidence estimation steps can also influence the quality of the pipeline’s final result. Traditional algorithms in this area were not designed by prioritizing scalability via parallelism, without sacrificing accuracy, which are crucial for WAM, and addressed in this thesis.

  Thus, our main contribution is proposing novel solutions in scenarios where traditional image abnormality detection / restoration approaches are inadequate to address the signal processing and computational challenges involved.

• Subjects / Keywords

  • Parallel Algorithms
  • Unsupervised Learning
  • Deep Learning
  • Brain Lesion Detection
  • Autoencoder
  • Graphics Processing Unit
  • High Performance Computing
  • Mach Band Effect
  • Subsidence Detection
  • Wide Area Monitoring
  • False Contour Detection
  • Computer Vision
  • InSAR Filtering
  • Image Filtering
  • Satellite Imagery
  • Interferometric Synthetic Aperture Radar
  • Machine Learning
  • Image Segmentation
  • Artificial Intelligence
  • Feature Extraction
  • Image Artifact
  • Image Abnormality Detection
  • Parallel Processing
  • Denoising
  • Image Processing
  • Landslide Prediction
  • Generative Model
  • Perceptual Masking
  • Medical Imaging Algorithm
  • Image Restoration
  • Mixture Density Network
  • Quantization Artifact
  • Convolutional Neural Network
  • Magnetic Resonance Imaging
  • Banding Artifact
  • White Matter Injury
  • High Dynamic Range
  • Outlier Detection
  • Preterm Infant
  • Debanding
  • Dithering
  • Signal Processing
  • Remote Sensing
  • InSAR Coherence Estimation

• Graduation date

  Fall 2020

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-pzk9-6119

• License

  Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.