Generalized Intersection Algorithms with Fixpoints for Image Decomposition Learning

• URL: http://arxiv.org/abs/2010.08661v1
• Date: Fri, 16 Oct 2020 22:55:34 GMT
• Title: Generalized Intersection Algorithms with Fixpoints for Image Decomposition Learning
• Authors: Robin Richter, Duy H. Thai and Stephan F. Huckemann
• Abstract summary: We formalize a general class of intersection point problems encompassing a wide range of (learned) image decomposition models. This class generalizes classical model-based variational problems, such as TV-l2 -model or the more general TV-Hilbert model.
• Score: 1.237556184089774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In image processing, classical methods minimize a suitable functional that balances between computational feasibility (convexity of the functional is ideal) and suitable penalties reflecting the desired image decomposition. The fact that algorithms derived from such minimization problems can be used to construct (deep) learning architectures has spurred the development of algorithms that can be trained for a specifically desired image decomposition, e.g. into cartoon and texture. While many such methods are very successful, theoretical guarantees are only scarcely available. To this end, in this contribution, we formalize a general class of intersection point problems encompassing a wide range of (learned) image decomposition models, and we give an existence result for a large subclass of such problems, i.e. giving the existence of a fixpoint of the corresponding algorithm. This class generalizes classical model-based variational problems, such as the TV-l2 -model or the more general TV-Hilbert model. To illustrate the potential for learned algorithms, novel (non learned) choices within our class show comparable results in denoising and texture removal.

Related papers

• Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring [48.80983873199214]
  We develop a data-driven approach to model the saturated pixels by a learned latent map. Based on the new model, the non-blind deblurring task can be formulated into a maximum a posterior (MAP) problem. To estimate high-quality deblurred images without amplified artifacts, we develop a prior estimation network.
  arXiv  Detail & Related papers  (2023-08-10T12:53:30Z)
• An Optimization-based Deep Equilibrium Model for Hyperspectral Image Deconvolution with Convergence Guarantees [71.57324258813675]
  We propose a novel methodology for addressing the hyperspectral image deconvolution problem. A new optimization problem is formulated, leveraging a learnable regularizer in the form of a neural network. The derived iterative solver is then expressed as a fixed-point calculation problem within the Deep Equilibrium framework.
  arXiv  Detail & Related papers  (2023-06-10T08:25:16Z)
• Binary Image Skeletonization Using 2-Stage U-Net [0.0]
  We propose a new metric, M-CCORR, based on normalized correlation coefficients as an alternative to F1 for this challenge. In this paper, we use a 2-stage variant of the famous U-Net architecture to split the problem space into two sub-problems: shape minimization and corrective skeleton thinning. Our model produces results that are visually much better than the baseline SkelNetOn model.
  arXiv  Detail & Related papers  (2021-12-22T11:58:42Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction [4.2476585678737395]
  We propose a general learning based framework for solving nonsmooth non image reconstruction problems. We show that the proposed is-efficient convergence state-of-the-art methods in an image problems in training.
  arXiv  Detail & Related papers  (2020-07-22T07:59:07Z)
• 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)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)
• A Unified Weight Learning and Low-Rank Regression Model for Robust Complex Error Modeling [12.287346997617542]
  One of the most important problems in regression-based error model is modeling the complex representation error caused by various corruptions environment changes in images. In this paper, we propose a unified weight learning and low-rank approximation regression model, which enables the random noises contiguous occlusions in images to be treated simultaneously.
  arXiv  Detail & Related papers  (2020-05-10T09:50:14Z)
• Deep Unfolding Network for Image Super-Resolution [159.50726840791697]
  This paper proposes an end-to-end trainable unfolding network which leverages both learning-based methods and model-based methods. The proposed network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model.
  arXiv  Detail & Related papers  (2020-03-23T17:55:42Z)

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.