Adaptive Direction-Guided Structure Tensor Total Variation

• URL: http://arxiv.org/abs/2001.05717v1
• Date: Thu, 16 Jan 2020 09:49:29 GMT
• Title: Adaptive Direction-Guided Structure Tensor Total Variation
• Authors: Ezgi Demircan-Tureyen and Mustafa E. Kamasak
• Abstract summary: Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term. We propose an efficient preprocessor that captures the local geometry based on the structure tensor.
• Score: 6.396288020763144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV model is not applicable to the multi-directional images of real-world. In this study, we build a two-stage framework that brings adaptivity to DSTV. We design an alternative to STV, which encodes the first-order information within a local neighborhood under the guidance of spatially varying directional descriptors (i.e., orientation and the dose of anisotropy). In order to estimate those descriptors, we propose an efficient preprocessor that captures the local geometry based on the structure tensor. Through the extensive experiments, we demonstrate how beneficial the involvement of the directional information in STV is, by comparing the proposed method with the state-of-the-art analysis-based denoising models, both in terms of restoration quality and computational efficiency.

Related papers

• Adaptive Step-size Perception Unfolding Network with Non-local Hybrid Attention for Hyperspectral Image Reconstruction [0.39134031118910273]
  We propose an adaptive step-size perception unfolding network (ASPUN), a deep unfolding network based on FISTA algorithm. In addition, we design a Non-local Hybrid Attention Transformer(NHAT) module for fully leveraging the receptive field advantage of transformer. Experimental results show that our ASPUN is superior to the existing SOTA algorithms and achieves the best performance.
  arXiv  Detail & Related papers  (2024-07-04T16:09:52Z)
• Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models [96.97910688908956]
  We introduce the first zero-shot approach for Video Semantic (VSS) based on pre-trained diffusion models. We propose a framework tailored for VSS based on pre-trained image and video diffusion models. Experiments show that our proposed approach outperforms existing zero-shot image semantic segmentation approaches.
  arXiv  Detail & Related papers  (2024-05-27T08:39:38Z)
• Weighted structure tensor total variation for image denoising [0.5120567378386615]
  For image denoising problems, the structure tensor total variation (STV)-based models show good performances when compared with other competing regularization approaches. We employ the anisotropic weighted matrix introduced in the anisotropic total variation (ATV) model to improve the STV model. Our proposed weighted STV model can effectively capture local information from images and maintain their details during the denoising process.
  arXiv  Detail & Related papers  (2023-06-18T05:37:38Z)
• 2-D SSM: A General Spatial Layer for Visual Transformers [79.4957965474334]
  A central objective in computer vision is to design models with appropriate 2-D inductive bias. We leverage an expressive variation of the multidimensional State Space Model. Our approach introduces efficient parameterization, accelerated computation, and a suitable normalization scheme.
  arXiv  Detail & Related papers  (2023-06-11T09:41:37Z)
• An Adaptive and Stability-Promoting Layerwise Training Approach for Sparse Deep Neural Network Architecture [0.0]
  This work presents a two-stage adaptive framework for developing deep neural network (DNN) architectures that generalize well for a given training data set. In the first stage, a layerwise training approach is adopted where a new layer is added each time and trained independently by freezing parameters in the previous layers. We introduce a epsilon-delta stability-promoting concept as a desirable property for a learning algorithm and show that employing manifold regularization yields a epsilon-delta stability-promoting algorithm.
  arXiv  Detail & Related papers  (2022-11-13T09:51:16Z)
• CSformer: Bridging Convolution and Transformer for Compressive Sensing [65.22377493627687]
  This paper proposes a hybrid framework that integrates the advantages of leveraging detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing.
  arXiv  Detail & Related papers  (2021-12-31T04:37:11Z)
• HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning [74.76431541169342]
  Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. We propose a novel hierarchical semantic-visual adaptation (HSVA) framework to align semantic and visual domains. Experiments on four benchmark datasets demonstrate HSVA achieves superior performance on both conventional and generalized ZSL.
  arXiv  Detail & Related papers  (2021-09-30T14:27:50Z)
• Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems [75.77033270838926]
  vehicular channels exhibit structure that can be exploited for beam alignment with fewer channel measurements. We propose a deep learning-based technique to design a structured compressed sensing (CS) matrix.
  arXiv  Detail & Related papers  (2021-02-27T04:38:12Z)
• Data-efficient Alignment of Multimodal Sequences by Aligning Gradient Updates and Internal Feature Distributions [36.82512331179322]
  Recent research suggests that network components dealing with different modalities may overfit and generalize at different speeds, creating difficulties for training. We propose layer-wise adaptive rate scaling (LARS) to align the magnitudes of gradient updates in different layers and balance the pace of learning. We also use sequence-wise batch normalization (SBN) to align the internal feature distributions from different modalities.
  arXiv  Detail & Related papers  (2020-11-15T13:04:25Z)
• Semantic Change Detection with Asymmetric Siamese Networks [71.28665116793138]
  Given two aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries. This problem is vital in many earth vision related tasks, such as precise urban planning and natural resource management. We present an asymmetric siamese network (ASN) to locate and identify semantic changes through feature pairs obtained from modules of widely different structures.
  arXiv  Detail & Related papers  (2020-10-12T13:26:30Z)
• Nonlocal Adaptive Direction-Guided Structure Tensor Total Variation For Image Recovery [6.396288020763144]
  This paper is concerned with boosting the NLSTV regularization term through the use of directional priors. We propose a method that employs anisotropic Gaussian kernels to estimate directional features to be later used by our proposed model.
  arXiv  Detail & Related papers  (2020-08-28T06:58:35Z)

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.