Parameter-free structure-texture image decomposition by unrolling

• URL: http://arxiv.org/abs/2503.13354v1
• Date: Mon, 17 Mar 2025 16:35:04 GMT
• Title: Parameter-free structure-texture image decomposition by unrolling
• Authors: Laura Girometti, Jean-François Aujol, Antoine Guennec, Yann Traonmilin,
• Abstract summary: We present a neural network LPR-NET based on the unrolling of the Low Patch Rank model.<n>Despite being trained on synthetic images, numerical experiments show the ability of our network to generalize well when applied to natural images.
• Score: 0.8999666725996975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose a parameter-free and efficient method to tackle the structure-texture image decomposition problem. In particular, we present a neural network LPR-NET based on the unrolling of the Low Patch Rank model. On the one hand, this allows us to automatically learn parameters from data, and on the other hand to be computationally faster while obtaining qualitatively similar results compared to traditional iterative model-based methods. Moreover, despite being trained on synthetic images, numerical experiments show the ability of our network to generalize well when applied to natural images.

Related papers

• Unwarping Screen Content Images via Structure-texture Enhancement Network and Transformation Self-estimation [2.404130767806698]
  We propose a structure-texture enhancement network (STEN) with transformation self-estimation for screen content images (SCIs) STEN integrates a B-spline implicit neural representation module and a transformation error estimation and self-correction algorithm. Experiments on public SCI datasets demonstrate that our approach significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-04-21T13:59:44Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Semi-sparsity Priors for Image Structure Analysis and Extraction [3.130722489512822]
  We propose a semi-sparse regularization framework for image structural analysis and extraction. We show that it is capable of preserving image structures without introducing notorious staircase artifacts. We also introduce an efficient numerical solution based on an direction method of multipliers (ADMM) algorithm.
  arXiv  Detail & Related papers  (2023-08-17T18:22:00Z)
• Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis [17.920305227880245]
  Our paper formulates triple vision tasks in a consistent manner using approximate analysis-by-synthesis. We show that our analysis-by-synthesis is much more robust than conventional neural networks when evaluated on real-world images.
  arXiv  Detail & Related papers  (2023-05-31T18:45:02Z)
• Neural inverse procedural modeling of knitting yarns from images [6.114281140793954]
  We show that the complexity of yarn structures can be better encountered in terms of ensembles of networks that focus on individual characteristics. We demonstrate that the combination of a carefully designed parametric, procedural yarn model with respective network ensembles as well as loss functions even allows robust parameter inference.
  arXiv  Detail & Related papers  (2023-03-01T00:56:39Z)
• DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator [53.57431705309919]
  ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. We develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features. We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets.
  arXiv  Detail & Related papers  (2022-11-09T14:57:27Z)
• Pushing the Efficiency Limit Using Structured Sparse Convolutions [82.31130122200578]
  We propose Structured Sparse Convolution (SSC), which leverages the inherent structure in images to reduce the parameters in the convolutional filter. We show that SSC is a generalization of commonly used layers (depthwise, groupwise and pointwise convolution) in efficient architectures'' Architectures based on SSC achieve state-of-the-art performance compared to baselines on CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet classification benchmarks.
  arXiv  Detail & Related papers  (2022-10-23T18:37:22Z)
• 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)
• Out-of-distribution Generalization via Partial Feature Decorrelation [72.96261704851683]
  We present a novel Partial Feature Decorrelation Learning (PFDL) algorithm, which jointly optimize a feature decomposition network and the target image classification model. The experiments on real-world datasets demonstrate that our method can improve the backbone model's accuracy on OOD image classification datasets.
  arXiv  Detail & Related papers  (2020-07-30T05:48:48Z)
• Learning Local Complex Features using Randomized Neural Networks for Texture Analysis [0.1474723404975345]
  We present a new approach that combines a learning technique and the Complex Network (CN) theory for texture analysis. This method takes advantage of the representation capacity of CN to model a texture image as a directed network. This neural network has a single hidden layer and uses a fast learning algorithm, which is able to learn local CN patterns for texture characterization.
  arXiv  Detail & Related papers  (2020-07-10T23:18:01Z)
• Region-adaptive Texture Enhancement for Detailed Person Image Synthesis [86.69934638569815]
  RATE-Net is a novel framework for synthesizing person images with sharp texture details. The proposed framework leverages an additional texture enhancing module to extract appearance information from the source image. Experiments conducted on DeepFashion benchmark dataset have demonstrated the superiority of our framework compared with existing networks.
  arXiv  Detail & Related papers  (2020-05-26T02:33:21Z)

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.