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  3. Computational Imaging (LTAT.02.025)
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Computational Imaging 2022/23 spring

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Project Date: Tuesday, May 23 from 10:15-12:00

Project Ideas

P1. Exploring and Comparing Methods of Demosaicing.

P2. Measuring the no-parallax point of the eye.

P3. Extreme Low-Light Image Denoising.

P4. Augmented reality - Applying noise and blur to the virtual content to make it appear as real objects.

P5. CNN-based Image Denoising.

P6. Obstruction-free Light Field Photography.

P7. Highly Dispersive Meta-Lens for Depth Estimation from Single-Shot Image.

P8. Motion Deblurring for Text.

P9. ADMM Applied to Bistatic Radar Tomography.

P10. Denoising of Abdominopelvic Computed Tomography Scans.

P11. Super-Resolution with Light Field Cameras.

P12. Computational Star-Tracking Compensation in Astrophotography.

P13. Single Image Dehazing for Underwater Imaging.

P14. Depth Estimation using Light-Field Camera.

P15. 3D distance sensing using optimized point spread function.

P16. Artificial Bokeh Blur for Portrait Imaging using Depth Estimation.

P17. 3D Imaging of Atom Clouds.

P18. Depth and All-In-Focus Extraction from Aperture-Focal Stacks via Convex Optimization.

P18. Denoising Image Corrupted with Gaussian Noise using SURE-LET Approach.

P19. Depth Estimation and disruption Simulation (e.g. scattering, fog).

P20. Aerial Image Denoising.

P21. Auto White Balance with Convolutional Color Constancy.

P22. Scene Representations from Focal Stack for Depth Estimation.

P23. Regularization Methods for Multi-Frame Super-Resolution.

P24. End-to-end optimization of a lensless imaging system.

P25. Computational Ultrasound Imaging.

P26. Synthetic Depth of Field for All-in-Focus Images.

P27. Object Tracking.

P28. Algorithms for Mote Carlo Denoising.

P29. Speckle-embedded propagation models for holographic displays.

P30. Convolutional Dictionary Learning for Arrhythmia Classication.

P31. Airborne Synthetic Aperture 3D Image Reconstruction of Underwater Environments.

P32. Super Resolution Display for Virtual Reality.

P32. Optical flow estimation with regularization and ADMM.

P33. End-to-end optimization of coded aperture for extended depth of field.

  • Institute of Computer Science
  • Faculty of Science and Technology
  • University of Tartu
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