Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision

• URL: http://arxiv.org/abs/2311.15497v3
• Date: Fri, 19 Jan 2024 02:45:44 GMT
• Title: Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision
• Authors: Gabriel De Araujo, Shanlin Sun, Xiaohui Xie
• Abstract summary: We propose a single framework for image registration based on deep neural networks and optimization. We show improvements of up to 1.6% in test data, while maintaining the same inference time, and a substantial 1.0% points performance gain in deformation field smoothness.
• Score: 13.242184146186974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image registration has traditionally been done using two distinct approaches: learning based methods, relying on robust deep neural networks, and optimization-based methods, applying complex mathematical transformations to warp images accordingly. Of course, both paradigms offer advantages and disadvantages, and, in this work, we seek to combine their respective strengths into a single streamlined framework, using the outputs of the learning based method as initial parameters for optimization while prioritizing computational power for the image pairs that offer the greatest loss. Our investigations showed improvements of up to 1.6% in test data, while maintaining the same inference time, and a substantial 1.0% points performance gain in deformation field smoothness.

Related papers

• Adaptive Preference Scaling for Reinforcement Learning with Human Feedback [103.36048042664768]
  Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values. We propose a novel adaptive preference loss, underpinned by distributionally robust optimization (DRO) Our method is versatile and can be readily adapted to various preference optimization frameworks.
  arXiv  Detail & Related papers  (2024-06-04T20:33:22Z)
• NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency [1.1470070927586016]
  We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. We show that our images are more consistent with original data distribution than prior works by visualization and additional analysis.
  arXiv  Detail & Related papers  (2023-06-29T03:43:29Z)
• Explainable bilevel optimization: an application to the Helsinki deblur challenge [1.1470070927586016]
  We present a bilevel optimization scheme for the solution of a general image deblurring problem. A parametric variational-like approach is encapsulated within a machine learning scheme to provide a high quality reconstructed image.
  arXiv  Detail & Related papers  (2022-10-18T11:36:37Z)
• Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration [0.37501702548174964]
  This work formulates image registration as a meta-learning algorithm. Experiments are presented in this paper using clinical transrectal ultrasound image data from 108 prostate cancer patients.
  arXiv  Detail & Related papers  (2022-07-22T10:30:00Z)
• Towards a Unified Approach to Homography Estimation Using Image Features and Pixel Intensities [0.0]
  The homography matrix is a key component in various vision-based robotic tasks. Traditionally, homography estimation algorithms are classified into feature- or intensity-based. This paper proposes a new hybrid method that unifies both classes into a single nonlinear optimization procedure.
  arXiv  Detail & Related papers  (2022-02-20T02:47:05Z)
• An Adaptive Framework for Learning Unsupervised Depth Completion [59.17364202590475]
  We present a method to infer a dense depth map from a color image and associated sparse depth measurements. We show that regularization and co-visibility are related via the fitness of the model to data and can be unified into a single framework.
  arXiv  Detail & Related papers  (2021-06-06T02:27:55Z)
• Shared Prior Learning of Energy-Based Models for Image Reconstruction [69.72364451042922]
  We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data. In the absence of ground truth data, we change the loss functional to a patch-based Wasserstein functional. In shared prior learning, both aforementioned optimal control problems are optimized simultaneously with shared learned parameters of the regularizer.
  arXiv  Detail & Related papers  (2020-11-12T17:56:05Z)
• FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning [64.32306537419498]
  We propose a novel learned feature-based refinement and augmentation method that produces a varied set of complex transformations. These transformations also use information from both within-class and across-class representations that we extract through clustering. We demonstrate that our method is comparable to current state of art for smaller datasets while being able to scale up to larger datasets.
  arXiv  Detail & Related papers  (2020-07-16T17:55:31Z)
• A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding [57.1077544780653]
  We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end.
  arXiv  Detail & Related papers  (2020-06-26T08:34:54Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)
• Adaptive binarization based on fuzzy integrals [7.4836284046629995]
  This document presents a new adaptive binarization technique based on fuzzy integral images through an efficient design of a modified SAT for fuzzy integrals. The experimental results show that the proposed methodology have produced an image quality thresholding often better than traditional algorithms and saliency neural networks.
  arXiv  Detail & Related papers  (2020-03-04T18:30:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.