56 research outputs found
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table
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
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μ£Ό.For the image restoration problem, recent variational approaches exploiting nonlocal information of an image have demonstrated significant improvements compared with traditional methods utilizing local features. Hence, we propose two variational models based on the sparse representation of image groups, to recover images with non-Gaussian noise. The proposed models are designed to restore image with Cauchy noise and speckle noise, respectively. To achieve efficient and stable performance, an alternating optimization scheme with a novel initialization technique is used. Experimental results suggest that the proposed methods outperform other methods in terms of both visual perception and numerical indexes.μμ 볡μ λ¬Έμ μμ, μμμ λΉκ΅μ§μ μΈ μ 보λ₯Ό νμ©νλ μ΅κ·Όμ λ€μν μ κ·Ό λ°©μμ κ΅μ§μ μΈ νΉμ±μ νμ©νλ κΈ°μ‘΄ λ°©λ²κ³Ό λΉκ΅νμ¬ ν¬κ² κ°μ λμλ€. λ°λΌμ, μ°λ¦¬λ λΉκ°μ°μμ μ‘μ μμμ 볡μνκΈ° μν΄ μμ κ·Έλ£Ή ν¬μ ννμ κΈ°λ°ν λ κ°μ§ λ³λΆλ²μ λͺ¨λΈμ μ μνλ€. μ μλ λͺ¨λΈμ κ°κ° μ½μ μ‘μκ³Ό μ€νν΄ μ‘μ μμμ 볡μνλλ‘ μ€κ³λμλ€. ν¨μ¨μ μ΄κ³ μμ μ μΈ μ±λ₯μ λ¬μ±νκΈ° μν΄, κ΅λ λ°©ν₯ μΉμλ²κ³Ό μλ‘μ΄ μ΄κΈ°ν κΈ°μ μ΄ μ¬μ©λλ€. μ€ν κ²°κ³Όλ μ μλ λ°©λ²μ΄ μκ°μ μΈ μΈμκ³Ό μμΉμ μΈ μ§ν λͺ¨λμμ λ€λ₯Έ λ°©λ²λ³΄λ€ μ°μν¨μ λνλΈλ€.1 Introduction 1
2 Preliminaries 5
2.1 Cauchy Noise 5
2.1.1 Introduction 6
2.1.2 Literature Review 7
2.2 Speckle Noise 9
2.2.1 Introduction 10
2.2.2 Literature Review 13
2.3 GSR 15
2.3.1 Group Construction 15
2.3.2 GSR Modeling 16
2.4 ADMM 17
3 Proposed Models 19
3.1 Proposed Model 1: GSRC 19
3.1.1 GSRC Modeling via MAP Estimator 20
3.1.2 Patch Distance for Cauchy Noise 22
3.1.3 The ADMM Algorithm for Solving (3.7) 22
3.1.4 Numerical Experiments 28
3.1.5 Discussion 45
3.2 Proposed Model 2: GSRS 48
3.2.1 GSRS Modeling via MAP Estimator 50
3.2.2 Patch Distance for Speckle Noise 52
3.2.3 The ADMM Algorithm for Solving (3.42) 53
3.2.4 Numerical Experiments 56
3.2.5 Discussion 69
4 Conclusion 74
Abstract (in Korean) 84Docto
Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling
L'abstract Γ¨ presente nell'allegato / the abstract is in the attachmen
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