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

Reduction of speckle noise by using an adaptive window

Speckle noise is a multiplicative noise that results from random fluctuations of signals when they are reflected on a surface. This research article proposes a techniqueto reduce speckle noise by using classic statistical filters and a size-adaptivewindow. The advantage of using the mentioned window is that such filters show adequate performance at reducing noise and preserving edges. In fact, the resultss how that the measurement of absolute performance that can be obtained with thiswindow is better than the best results when the window is not used. Such better performance is expressed in terms of the signal / noise ratio, the edge improvement index and the mean square error

Modified Fuzzy-Anisotropic Gaussian Kernel and CRB in Denoising SAR Image

Radar speckle noise is often modeled as multiplicative noise for such that higher the intensity higher the speckle noise. As a result, the brighter pixel values are having more noise. The presence of speckle not only complicates visual image interpretation but also the classification of automated image is difficult in corrupted SAR image. Therefore, speckle has to be reduced before analyzing the SAR image.Thus, speckle is the main problem (mingled) in Synthetic Aperture Radar (SAR) images. Speckle is existed due to constructive and destructive interference of coherent signal. In order to reduce it, we approach enhanced kernel based filter. Till there are so many techniques are developed to remove speckle content in SAR system. But no proper technique as been developed to remove speckle content completely. In our project MMSE based filter technique is used. We propose a new integrated Fuzzy Anisotropic Gaussian Kernel (FAGK) for denoising Synthetic Aperture Radar (SAR) Images. Here, texture information lies on principal orientation should be multiplied with fuzzy membership function through the anisotropic Gaussian kernel. It presents Cramer -Rao Bound (CRB) which can be estimated by taking ensemble of texture modeled covariance matrix for different denoising methods. Later, CRB can be found for an index of speckle suppression. Thus, developed filter gives good result in preservation of texture and in structure enhancement. It also presents evaluation of speckle suppression ability, where an index named SMPI (Speckle Suppression and Mean Preservation Index). It compares CRB for the evaluation of SMPI index with different denoising method

Reduction of speckle noise by using an adaptive window

Speckle noise is a multiplicative noise that results from random fluctuations of signals when they are reflected on a surface. This research article proposes a techniqueto reduce speckle noise by using classic statistical filters and a size-adaptivewindow. The advantage of using the mentioned window is that such filters show adequate performance at reducing noise and preserving edges. In fact, the resultss how that the measurement of absolute performance that can be obtained with thiswindow is better than the best results when the window is not used. Such better performance is expressed in terms of the signal / noise ratio, the edge improvement index and the mean square error