Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis

The flare-productivity of an active region is observed to be related to its spatial complexity. Mount Wilson or McIntosh sunspot classifications measure such complexity but in a categorical way, and may therefore not use all the information present in the observations. Moreover, such categorical schemes hinder a systematic study of an active region's evolution for example. We propose fine-scale quantitative descriptors for an active region's complexity and relate them to the Mount Wilson classification. We analyze the local correlation structure within continuum and magnetogram data, as well as the cross-correlation between continuum and magnetogram data. We compute the intrinsic dimension, partial correlation, and canonical correlation analysis (CCA) of image patches of continuum and magnetogram active region images taken from the SOHO-MDI instrument. We use masks of sunspots derived from continuum as well as larger masks of magnetic active regions derived from the magnetogram to analyze separately the core part of an active region from its surrounding part. We find the relationship between complexity of an active region as measured by Mount Wilson and the intrinsic dimension of its image patches. Partial correlation patterns exhibit approximately a third-order Markov structure. CCA reveals different patterns of correlation between continuum and magnetogram within the sunspots and in the region surrounding the sunspots. These results also pave the way for patch-based dictionary learning with a view towards automatic clustering of active regions.Comment: Accepted for publication in the Journal of Space Weather and Space Climate (SWSC). 23 pages, 11 figure

Video modeling via implicit motion representations

Video modeling refers to the development of analytical representations for explaining the intensity distribution in video signals. Based on the analytical representation, we can develop algorithms for accomplishing particular video-related tasks. Therefore video modeling provides us a foundation to bridge video data and related-tasks. Although there are many video models proposed in the past decades, the rise of new applications calls for more efficient and accurate video modeling approaches.;Most existing video modeling approaches are based on explicit motion representations, where motion information is explicitly expressed by correspondence-based representations (i.e., motion velocity or displacement). Although it is conceptually simple, the limitations of those representations and the suboptimum of motion estimation techniques can degrade such video modeling approaches, especially for handling complex motion or non-ideal observation video data. In this thesis, we propose to investigate video modeling without explicit motion representation. Motion information is implicitly embedded into the spatio-temporal dependency among pixels or patches instead of being explicitly described by motion vectors.;Firstly, we propose a parametric model based on a spatio-temporal adaptive localized learning (STALL). We formulate video modeling as a linear regression problem, in which motion information is embedded within the regression coefficients. The coefficients are adaptively learned within a local space-time window based on LMMSE criterion. Incorporating a spatio-temporal resampling and a Bayesian fusion scheme, we can enhance the modeling capability of STALL on more general videos. Under the framework of STALL, we can develop video processing algorithms for a variety of applications by adjusting model parameters (i.e., the size and topology of model support and training window). We apply STALL on three video processing problems. The simulation results show that motion information can be efficiently exploited by our implicit motion representation and the resampling and fusion do help to enhance the modeling capability of STALL.;Secondly, we propose a nonparametric video modeling approach, which is not dependent on explicit motion estimation. Assuming the video sequence is composed of many overlapping space-time patches, we propose to embed motion-related information into the relationships among video patches and develop a generic sparsity-based prior for typical video sequences. First, we extend block matching to more general kNN-based patch clustering, which provides an implicit and distributed representation for motion information. We propose to enforce the sparsity constraint on a higher-dimensional data array signal, which is generated by packing the patches in the similar patch set. Then we solve the inference problem by updating the kNN array and the wanted signal iteratively. Finally, we present a Bayesian fusion approach to fuse multiple-hypothesis inferences. Simulation results in video error concealment, denoising, and deartifacting are reported to demonstrate its modeling capability.;Finally, we summarize the proposed two video modeling approaches. We also point out the perspectives of implicit motion representations in applications ranging from low to high level problems

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

Point Cloud Normal Estimation with Graph-Convolutional Neural Networks

Surface normal estimation is a basic task for many point cloud processing algorithms. However, it can be challenging to capture the local geometry of the data, especially in presence of noise. Recently, deep learning approaches have shown promising results. Nevertheless, applying convolutional neural networks to point clouds is not straightforward, due to the irregular positioning of the points. In this paper, we propose a normal estimation method based on graph-convolutional neural networks to deal with such irregular point cloud domain. The graph-convolutional layers build hierarchies of localized features to solve the estimation problem. We show state-ofthe-art performance and robust results even in presence of noise

초점 스택에서 3D 깊이 재구성 및 깊이 개선

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 신영길.Three-dimensional (3D) depth recovery from two-dimensional images is a fundamental and challenging objective in computer vision, and is one of the most important prerequisites for many applications such as 3D measurement, robot location and navigation, self-driving, and so on. Depth-from-focus (DFF) is one of the important methods to reconstruct a 3D depth in the use of focus information. Reconstructing a 3D depth from texture-less regions is a typical issue associated with the conventional DFF. Further more, it is difficult for the conventional DFF reconstruction techniques to preserve depth edges and fine details while maintaining spatial consistency. In this dissertation, we address these problems and propose an DFF depth recovery framework which is robust over texture-less regions, and can reconstruct a depth image with clear edges and fine details. The depth recovery framework proposed in this dissertation is composed of two processes: depth reconstruction and depth refinement. To recovery an accurate 3D depth, We first formulate the depth reconstruction as a maximum a posterior (MAP) estimation problem with the inclusion of matting Laplacian prior. The nonlocal principle is adopted during the construction stage of the matting Laplacian matrix to preserve depth edges and fine details. Additionally, a depth variance based confidence measure with the combination of the reliability measure of focus measure is proposed to maintain the spatial smoothness, such that the smooth depth regions in initial depth could have high confidence value and the reconstructed depth could be more derived from the initial depth. As the nonlocal principle breaks the spatial consistency, the reconstructed depth image is spatially inconsistent. Meanwhile, it suffers from texture-copy artifacts. To smooth the noise and suppress the texture-copy artifacts introduced in the reconstructed depth image, we propose a closed-form edge-preserving depth refinement algorithm that formulates the depth refinement as a MAP estimation problem using Markov random fields (MRFs). With the incorporation of pre-estimated depth edges and mutual structure information into our energy function and the specially designed smoothness weight, the proposed refinement method can effectively suppress noise and texture-copy artifacts while preserving depth edges. Additionally, with the construction of undirected weighted graph representing the energy function, a closed-form solution is obtained by using the Laplacian matrix corresponding to the graph. The proposed framework presents a novel method of 3D depth recovery from a focal stack. The proposed algorithm shows the superiority in depth recovery over texture-less regions owing to the effective variance based confidence level computation and the matting Laplacian prior. Additionally, this proposed reconstruction method can obtain a depth image with clear edges and fine details due to the adoption of nonlocal principle in the construct]ion of matting Laplacian matrix. The proposed closed-form depth refinement approach shows that the ability in noise removal while preserving object structure with the usage of common edges. Additionally, it is able to effectively suppress texture-copy artifacts by utilizing mutual structure information. The proposed depth refinement provides a general idea for edge-preserving image smoothing, especially for depth related refinement such as stereo vision. Both quantitative and qualitative experimental results show the supremacy of the proposed method in terms of robustness in texture-less regions, accuracy, and ability to preserve object structure while maintaining spatial smoothness.Chapter 1 Introduction 1 1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 2 Related Works 9 2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Principle of depth-from-focus . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Focus measure operators . . . . . . . . . . . . . . . . . . . 12 2.3 Depth-from-focus reconstruction . . . . . . . . . . . . . . . . . . 14 2.4 Edge-preserving image denoising . . . . . . . . . . . . . . . . . . 23 Chapter 3 Depth-from-Focus Reconstruction using Nonlocal Matting Laplacian Prior 38 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Image matting and matting Laplacian . . . . . . . . . . . . . . . 40 3.3 Depth-from-focus . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4 Depth reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.4.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . 47 3.4.2 Likelihood model . . . . . . . . . . . . . . . . . . . . . . . 48 3.4.3 Nonlocal matting Laplacian prior model . . . . . . . . . . 50 3.5 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.5.2 Data configuration . . . . . . . . . . . . . . . . . . . . . . 55 3.5.3 Reconstruction results . . . . . . . . . . . . . . . . . . . . 56 3.5.4 Comparison between reconstruction using local and nonlocal matting Laplacian . . . . . . . . . . . . . . . . . . . 56 3.5.5 Spatial consistency analysis . . . . . . . . . . . . . . . . . 59 3.5.6 Parameter setting and analysis . . . . . . . . . . . . . . . 59 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Chapter 4 Closed-form MRF-based Depth Refinement 63 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.3 Closed-form solution . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4 Edge preservation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.5 Texture-copy artifacts suppression . . . . . . . . . . . . . . . . . 73 4.6 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Chapter 5 Evaluation 82 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.2 Evaluation metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.3 Evaluation on synthetic datasets . . . . . . . . . . . . . . . . . . 84 5.4 Evaluation on real scene datasets . . . . . . . . . . . . . . . . . . 89 5.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.6 Computational performances . . . . . . . . . . . . . . . . . . . . 93 Chapter 6 Conclusion 96 Bibliography 99Docto

Low-rankness transfer for denoising Sentinel-1 SAR images

International audienceThis paper introduces a new algorithm for denoising SAR images. It is directly applicable to Sentinel-1 GRD images, without the need for single-look complex operations. The algorithm builds on the non-local patch matching idea for statistical denoising, similar to the SAR-BM3D and NL-SAR algorithms, but introduces two corrections : 1. A non-uniform prior for the reflectance values is used for the patch matching, thus allowing a better fit to the data, and 2. Denoising is performed in singular values space, with a prior distribution of expected "clean" singular values learned and transfered from optical images. The denoised SAR images show reduced amount of speckle compared to alternative methods