2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDIMT-1.12
Paper Title MOTIVATING BILEVEL APPROACHES TO FILTER LEARNING: A CASE STUDY
Authors Caroline Crockett, Jeffrey A. Fessler, University of Michigan, United States
SessionIMT-1: Computational Imaging Learning-based Models
LocationArea J
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Computational Imaging Methods and Models: Learning-Based Models
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The recent trend in regularization methods for inverse problems is to replace handcrafted sparsifying operators with data-driven approaches. Although using such machine learning techniques often improves image reconstruction methods, the results can depend significantly on the learning methodology. This paper compares two supervised learning methods. First, the paper considers a transform learning approach and, to learn the transform, introduces a variant on the Procrustes method for wide matrices with orthogonal rows. Second, we consider a bilevel convolutional filter learning approach. Numerical experiments show the learned transform performs worse for denoising than both the handcrafted finite difference transform and the learned filters, which perform similarly. Our results motivate the use of bilevel learning.