Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment

• URL: http://arxiv.org/abs/2310.17801v1
• Date: Thu, 26 Oct 2023 22:17:37 GMT
• Title: Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment
• Authors: Jie Wei, Weicong Feng, Erik Blasch, Erika Ardiles-Cruz, Haibin Ling
• Abstract summary: It is important to quantify Damage Assessment for Human Assistance and Disaster Response applications. In this paper, an image prior and posterior conditional probability (IP2CP) is developed as an effective computational imaging representation. The matching pre- and post-disaster images are effectively encoded into one image that is then processed using deep learning approaches to determine the damage levels.
• Score: 51.631659414455825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is important to quantify Damage Assessment (DA) for Human Assistance and Disaster Response (HADR) applications. In this paper, to achieve efficient and scalable DA in HADR, an image prior and posterior conditional probability (IP2CP) is developed as an effective computational imaging representation. Equipped with the IP2CP representation, the matching pre- and post-disaster images are effectively encoded into one image that is then processed using deep learning approaches to determine the damage levels. Two scenarios of crucial importance for the practical use of DA in HADR applications are examined: pixel-wise semantic segmentation and patch-based contrastive learning-based global damage classification. Results achieved by IP2CP in both scenarios demonstrate promising performances, showing that our IP2CP-based methods within the deep learning framework can effectively achieve data and computational efficiency, which is of utmost importance for the DA in HADR applications.

Related papers

• Revisiting Disparity from Dual-Pixel Images: Physics-Informed Lightweight Depth Estimation [3.6337378417255177]
  We propose a lightweight disparity estimation method based on a completion-based network. By modeling the DP-specific disparity error parametrically and using it for sampling during training, the network acquires the unique properties of DP. As a result, the proposed method achieved state-of-the-art results while reducing the overall system size to 1/5 of that of the conventional method.
  arXiv  Detail & Related papers  (2024-11-06T09:03:53Z)
• Relationship Quantification of Image Degradations [72.98190570967937]
  Degradation Relationship Index (DRI) is defined as the mean drop rate difference in the validation loss between two models. DRI always predicts performance improvement by using the specific degradation as an auxiliary to train models. We propose a simple but effective method to estimate whether the given degradation combinations could improve the performance on the anchor degradation.
  arXiv  Detail & Related papers  (2022-12-08T09:05:19Z)
• Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition [9.927965682734069]
  A light network named the Kernel Inversed Pyramidal Resizing Network (KIPRN) is introduced for image resizing. In KIPRN, pyramidal convolution and kernel inversed convolution are specifically designed to mine discriminative information. Extensive results demonstrate that KIPRN can generally improve the pavement distress recognition of CNN models.
  arXiv  Detail & Related papers  (2022-12-04T10:40:40Z)
• Real-World Image Super-Resolution by Exclusionary Dual-Learning [98.36096041099906]
  Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input. Deep learning-based methods have achieved promising restoration quality on real-world image super-resolution datasets. We propose Real-World image Super-Resolution by Exclusionary Dual-Learning (RWSR-EDL) to address the feature diversity in perceptual- and L1-based cooperative learning.
  arXiv  Detail & Related papers  (2022-06-06T13:28:15Z)
• Robust and Decomposable Average Precision for Image Retrieval [0.0]
  In image retrieval, standard evaluation metrics rely on score ranking, e.g. average precision (AP) In this paper, we introduce a method for robust and decomposable average precision (ROADMAP) We address two major challenges for end-to-end training of deep neural networks with AP: non-differentiability and non-decomposability.
  arXiv  Detail & Related papers  (2021-10-01T12:00:43Z)
• Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images [70.13735569016752]
  We investigate the robustness of deep learning in CT image reconstruction by showing false negative and false positive lesion cases. We propose a data consistent reconstruction (DCR) method to improve their image quality, which combines the advantages of compressed sensing and deep learning. The efficacy of the proposed method is demonstrated in cone-beam CT with truncated data, limited-angle data and sparse-view data, respectively.
  arXiv  Detail & Related papers  (2020-05-20T13:30:49Z)
• DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning [122.51237307910878]
  We develop methods for few-shot image classification from a new perspective of optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations. To generate the important weights of elements in the formulation, we design a cross-reference mechanism.
  arXiv  Detail & Related papers  (2020-03-15T08:13:16Z)
• BP-DIP: A Backprojection based Deep Image Prior [49.375539602228415]
  We propose two image restoration approaches: (i) Deep Image Prior (DIP), which trains a convolutional neural network (CNN) from scratch in test time using the degraded image; and (ii) a backprojection (BP) fidelity term, which is an alternative to the standard least squares loss that is usually used in previous DIP works. We demonstrate the performance of the proposed method, termed BP-DIP, on the deblurring task and show its advantages over the plain DIP, with both higher PSNR values and better inference run-time.
  arXiv  Detail & Related papers  (2020-03-11T17:09:12Z)

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.