Performance analysis of weighted low rank model with sparse image histograms for face recognition under lowlevel illumination and occlusion

• URL: http://arxiv.org/abs/2007.12362v1
• Date: Fri, 24 Jul 2020 05:59:28 GMT
• Title: Performance analysis of weighted low rank model with sparse image histograms for face recognition under lowlevel illumination and occlusion
• Authors: K.V. Sridhar and Raghu vamshi Hemadri
• Abstract summary: The purpose of Low-rank approximation matrix (LRMA) models is to recover the underlying low-rank matrix from its degraded observation. In this paper, a comparison of the low-rank approximation of LRMARPC- and WSNM is brought out. The paper also discusses the trends from the experimental results performed through the application of these algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a broad range of computer vision applications, the purpose of Low-rank matrix approximation (LRMA) models is to recover the underlying low-rank matrix from its degraded observation. The latest LRMA methods - Robust Principal Component Analysis (RPCA) resort to using the nuclear norm minimization (NNM) as a convex relaxation of the non-convex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We use a more flexible model, namely the Weighted Schatten p-Norm Minimization (WSNM), to generalize the NNM to the Schatten p-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives a better approximation to the original low-rank assumption but also considers the importance of different rank components. In this paper, a comparison of the low-rank recovery performance of two LRMA algorithms- RPCA and WSNM is brought out on occluded human facial images. The analysis is performed on facial images from the Yale database and over own database , where different facial expressions, spectacles, varying illumination account for the facial occlusions. The paper also discusses the prominent trends observed from the experimental results performed through the application of these algorithms. As low-rank images sometimes might fail to capture the details of a face adequately, we further propose a novel method to use the image-histogram of the sparse images thus obtained to identify the individual in any given image. Extensive experimental results show, both qualitatively and quantitatively, that WSNM surpasses RPCA in its performance more effectively by removing facial occlusions, thus giving recovered low-rank images of higher PSNR and SSIM.

Related papers

• Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration [34.50287066865267]
  Posterior-Mean Rectified Flow (PMRF) is a simple yet highly effective algorithm that approximates this optimal estimator. We investigate the theoretical utility of PMRF and demonstrate that it consistently outperforms previous methods on a variety of image restoration tasks.
  arXiv  Detail & Related papers  (2024-10-01T05:54:07Z)
• HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models [38.74983301496911]
  Hyperspectral image (HSI) restoration aims at recovering clean images from degraded observations. Existing model-based methods have limitations in accurately modeling the complex image characteristics. This paper proposes an unsupervised HSI restoration framework with pre-trained diffusion model (HIR-Diff)
  arXiv  Detail & Related papers  (2024-02-24T17:15:05Z)
• MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces [51.42949911178461]
  We propose a method dubbed MetaF2N to fine-tune model parameters for adapting to the whole Natural image in a Meta-learning framework. Considering the gaps between the recovered faces and ground-truths, we deploy a MaskNet for adaptively predicting loss weights at different positions to reduce the impact of low-confidence areas.
  arXiv  Detail & Related papers  (2023-09-15T02:45:21Z)
• One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging [10.797632196651731]
  Low-dose computed tomography (CT) plays a significant role in reducing the radiation risk in clinical applications. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT imaging algorithms. We propose a fully unsupervised one sample diffusion model (OSDM)in projection domain for low-dose CT reconstruction. The results prove that OSDM is practical and effective model for reducing the artifacts and preserving the image quality.
  arXiv  Detail & Related papers  (2022-12-07T13:39:23Z)
• Unsupervised PET Reconstruction from a Bayesian Perspective [12.512270202705404]
  DeepRED is a typical representation that combines DIP and regularization by denoising (RED) In this article, we leverage DeepRED from a Bayesian perspective to reconstruct PET images from a single corrupted sinogram without any supervised or auxiliary information.
  arXiv  Detail & Related papers  (2021-10-29T06:32:21Z)
• Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling [139.25215100378284]
  We propose a hierarchical conditional flow (HCFlow) as a unified framework for image SR and image rescaling. HCFlow learns a mapping between HR and LR image pairs by modelling the distribution of the LR image and the rest high-frequency component simultaneously. To further enhance the performance, other losses such as perceptual loss and GAN loss are combined with the commonly used negative log-likelihood loss in training.
  arXiv  Detail & Related papers  (2021-08-11T16:11:01Z)
• Towards Top-Down Just Noticeable Difference Estimation of Natural Images [65.14746063298415]
  Just noticeable difference (JND) estimation mainly dedicates to modeling the visibility masking effects of different factors in spatial and frequency domains. In this work, we turn to a dramatically different way to address these problems with a top-down design philosophy. Our proposed JND model can achieve better performance than several latest JND models.
  arXiv  Detail & Related papers  (2021-08-11T06:51:50Z)
• 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)
• Perceptually Optimizing Deep Image Compression [53.705543593594285]
  Mean squared error (MSE) and $ell_p$ norms have largely dominated the measurement of loss in neural networks. We propose a different proxy approach to optimize image analysis networks against quantitative perceptual models.
  arXiv  Detail & Related papers  (2020-07-03T14:33:28Z)
• 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)

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.