A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method

Nonlocal Multiscale Single Image Statistics From Sentinel-1 SAR Data for High Resolution Bitemporal Forest Wind Damage Detection

Change detection of synthetic aperture radar (SAR) data is a challenge for high-resolution applications. This study presents a new nonlocal averaging approach (STAl'SAR) to reduce the speckle of single Sentinel-1 SAR images and statistical parameters derived from the image. The similarity of SAR pixels is based on the statistics of 3 x 3 window as represented by the mean, standard deviation, median, minimum, and maximum. K-means clustering is used to divide the SAR image in 30 similarity clusters. The nonlocal averaging is carried out within each cluster separately in magnitude order of the 3 x 3 window averages. The nonlocal filtering is applicable not only to the original pixel backscattering values but also to statistical parameters, such as standard deviation. The statistical parameters to be filtered can represent any window size, according to the need of the application. The nonlocally averaged standard deviation derived in two spatial resolutions, 3 x 3 and 7 x 7 windows, are demonstrated here for improving the resolution in which the forest damages can be detected using the VH polarized backscattering spatial variation change.Peer reviewe

Image Restoration for Remote Sensing: Overview and Toolbox

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS

Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling

L'abstract è presente nell'allegato / the abstract is in the attachmen

심층 신경망을 활용한 자동 영상 잡음 제거 기법

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 협동과정 계산과학전공, 2020. 8. 강명주.Noise removal in digital image data is a fundamental and important task in the field of image processing. The goal of the task is to remove noises from the given degraded images while maintaining essential details such as edges, curves, textures, etc. There have been various attempts on image denoising: mainly model-based methods such as filtering methods, total variation based methods, non-local mean based approaches. Deep learning have been attracting significant research interest as they have shown better results than the classical methods in almost all fields. Deep learning-based methods use a large amount of data to train a network for its own objective; in the image denoising case, in order to map the corrupted image to a desired clean image. In this thesis we proposed a new network architecture focusing on white Gaussian noise and real noise cancellation. Our model is a deep and wide network designed by constructing a basic block consisting of a mixture of various types of dilated convolutions and repeatedly stacking them. We did not use a batch normal layer to maintain the original own color information of each input data. Also skip connection was utilized so as not to lose the existing information. Through several experiments and comparisons, it was proved that the proposed network has better performance compared to the traditional and latest methods in image denoising.디지털 영상 데이터 내의 잡음 제거 및 감소는 열화된 영상의 노이즈를 제거하면서 모서리, 곡선, 질감 등과 같은 필수 세부 정보를 유지하는 것이 목적인 영상 처리 분야의 기본적이고 필수적인 작업이다. 딥러닝 기반의 영상 잡음 제거 방법들은 열화된 영상을 원하는 품질의 영상으로 매핑하도록 대용량의 데이터를 이용하여 네트워크를 지도학습하며 고전적인 방법들보다 뛰어난 결과를 보여주고 있다. 본 논문에서는 이미지 디노이징에 대한 여러 방법들을 조사했을 뿐만 아니라, 특히 백색 가우시안 잡음과 실제 잡음 제거 문제에 집중하면서 네트워크 아키텍처를 설계하고 실험하였다. 여러 형태의 딜레이티드 콘볼루션들을 혼합하여 기본 블록을 구성하고 이를 반복하여 쌓아서 설계한 네트워크를 제안하였고, 각각 본연의 색상을 유지할 수 있도록 여러 입력 영상을 하나로 묶어 구성하는 배치를 평준화하는 배치노멀 레이어는 사용하지 않았다. 그리고 블록이 여러 층 진행되는 동안 기존의 정보를 손실하지 않도록 스킵 커넥션을 사용하였다. 여러 실험과 기존에 제안된 방법과 최신 벤치 마크와의 비교를 통하여 제안한 네트워크가 노이즈 감소 및 제거 작업에서 기존의 방법들과 비교하여 우수한 성능을 가지고 있음을 입증하였다. 하지만 제안한 아키텍처방법의 한계점도 몇 가지 존재한다. 제안한 네트워크는 다운샘플링을 사용하지 않음으로써 정보 손실을 최소화하였지만 최신 벤치마크에 비하여 더 많은 추론 시간이 필요하여 실시간 작업에는 적용하기가 쉽지 않다. 실제 영상에는 단순한 잡음보다는 영상 획득, 저장 등과 같은 프로세스를 거치면서 여러 요인들로 인한 다양한 잡음, 블러와 같은 열화가 혼재 되어 있다. 실제 잡음에 대한 다양한 각도에서의 분석과 여러 모델링 실험, 그리고 영상 잡음 및 블러, 압축과 같은 복합 모델링이 필요하다. 향후에는 이러한 점들을 보완함으로써 성능을 향상시키고 네트워크의 조정을 통해 실시간으로 적용될 수 있음을 기대한다.1 Introduction 1 2 Review on Image Denoising Methods 4 2.1 Image Noise Models 4 2.2 Traditional Denoising Methods 8 2.2.1 TV-based regularization 9 2.2.2 Non-local regularization 9 2.2.3 Sparse representation 10 2.2.4 Low-rank minimization 10 2.3 CNN-based Denoising Methods 11 2.3.1 DnCNN 11 2.3.2 FFDNet 12 2.3.3 WDnCNN 12 2.3.4 DHDN 13 3 Proposed models 15 3.1 Related Works 15 3.1.1 Residual learning 15 3.1.2 Dilated convolution 16 3.2 Proposed Network Architecture 17 4 Experiments 21 4.1 Training Details 21 4.2 Synthetic Noise Reduction 23 4.2.1 Set12 denoising 24 4.2.2 Kodak24 and BSD68 denoising 30 4.3 Real Noise Reduction 34 4.3.1 DnD test results 35 4.3.2 NTIRE 2020 real image denoising challenge 42 5 Conclusion and Future Works 46 Abstract (in Korean) 54Docto

InSAR Deformation Analysis with Distributed Scatterers: A Review Complemented by New Advances

Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique able to measure deformation of the earth’s surface over large areas. InSAR deformation analysis uses two main categories of backscatter: Persistent Scatterers (PS) and Distributed Scatterers (DS). While PS are characterized by a high signal-to-noise ratio and predominantly occur as single pixels, DS possess a medium or low signal-to-noise ratio and can only be exploited if they form homogeneous groups of pixels that are large enough to allow for statistical analysis. Although DS have been used by InSAR since its beginnings for different purposes, new methods developed during the last decade have advanced the field significantly. Preprocessing of DS with spatio-temporal filtering allows today the use of DS in PS algorithms as if they were PS, thereby enlarging spatial coverage and stabilizing algorithms. This review explores the relations between different lines of research and discusses open questions regarding DS preprocessing for deformation analysis. The review is complemented with an experiment that demonstrates that significantly improved results can be achieved for preprocessed DS during parameter estimation if their statistical properties are used

Nonlinear Adaptive Diffusion Models for Image Denoising

Most of digital image applications demand on high image quality. Unfortunately, images often are degraded by noise during the formation, transmission, and recording processes. Hence, image denoising is an essential processing step preceding visual and automated analyses. Image denoising methods can reduce image contrast, create block or ring artifacts in the process of denoising. In this dissertation, we develop high performance non-linear diffusion based image denoising methods, capable to preserve edges and maintain high visual quality. This is attained by different approaches: First, a nonlinear diffusion is presented with robust M-estimators as diffusivity functions. Secondly, the knowledge of textons derived from Local Binary Patterns (LBP) which unify divergent statistical and structural models of the region analysis is utilized to adjust the time step of diffusion process. Next, the role of nonlinear diffusion which is adaptive to the local context in the wavelet domain is investigated, and the stationary wavelet context based diffusion (SWCD) is developed for performing the iterative shrinkage. Finally, we develop a locally- and feature-adaptive diffusion (LFAD) method, where each image patch/region is diffused individually, and the diffusivity function is modified to incorporate the Inverse Difference Moment as a local estimate of the gradient. Experiments have been conducted to evaluate the performance of each of the developed method and compare it to the reference group and to the state-of-the-art methods

Variable Splitting as a Key to Efficient Image Reconstruction

The problem of reconstruction of digital images from their degraded measurements has always been a problem of central importance in numerous applications of imaging sciences. In real life, acquired imaging data is typically contaminated by various types of degradation phenomena which are usually related to the imperfections of image acquisition devices and/or environmental effects. Accordingly, given the degraded measurements of an image of interest, the fundamental goal of image reconstruction is to recover its close approximation, thereby "reversing" the effect of image degradation. Moreover, the massive production and proliferation of digital data across different fields of applied sciences creates the need for methods of image restoration which would be both accurate and computationally efficient. Developing such methods, however, has never been a trivial task, as improving the accuracy of image reconstruction is generally achieved at the expense of an elevated computational burden. Accordingly, the main goal of this thesis has been to develop an analytical framework which allows one to tackle a wide scope of image reconstruction problems in a computationally efficient manner. To this end, we generalize the concept of variable splitting, as a tool for simplifying complex reconstruction problems through their replacement by a sequence of simpler and therefore easily solvable ones. Moreover, we consider two different types of variable splitting and demonstrate their connection to a number of existing approaches which are currently used to solve various inverse problems. In particular, we refer to the first type of variable splitting as Bregman Type Splitting (BTS) and demonstrate its applicability to the solution of complex reconstruction problems with composite, cross-domain constraints. As specific applications of practical importance, we consider the problem of reconstruction of diffusion MRI signals from sub-critically sampled, incomplete data as well as the problem of blind deconvolution of medical ultrasound images. Further, we refer to the second type of variable splitting as Fuzzy Clustering Splitting (FCS) and show its application to the problem of image denoising. Specifically, we demonstrate how this splitting technique allows us to generalize the concept of neighbourhood operation as well as to derive a unifying approach to denoising of imaging data under a variety of different noise scenarios