SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

Abstract

Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we present one new SAR image de-noising method based on shift invariant K-SVD and guided filter. The whole method consists of two steps. The first deals mainly with the noisy image with shift invariant K-SVD and obtaining the initial de-noised image. In the second step, we do the guided filtering for the initial de-noised image. Finally, we can recover the final de-noised image. Experimental results show that our method not only has better visual effects and objective evaluation, but can also save more detailed information such as image edge and texture when de-noising SAR images. The presented shift invariant K-SVD can be widely used in image processing, such as image fusion, edge detection and super-resolution reconstruction.

Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

“…In the SR model, the image is sparse and can be represented, or approximately represented, by one linear combination of a few atoms from the dictionary [14,28,29]. Suppose that the source image is I, and the over-complete dictionary is D ∈ R M×k , the sparse representation model can be formulated as follows [16,22].α = argmin…”

“…However, there is more detailed information in the remote sensing images than other kinds of images. When performing image fusion by the method based on SR, it may lose some discontinuous edge features [22], which leads to the loss of some useful information of fused images. In addition, image fusion based on SR also ignores the spatial information, which can reflect the image structure more directly and accurately.…”

Combination of Fast Finite Shear Wave Transform and Optimized Deep Convolutional Neural Network: A Better Method for Noise Reduction of Wetland Test Images

“…There are various classifications of image denoising methods, which can be broadly divided into: spatial domain-based denoising algorithms, such as Gaussian filtering [4], median filtering [5]; transform domain-based denoising algorithms, such as wavelet transform [6]; sparse representation-based denoising algorithms, such as discrete cosine transform (DCT) [7], K-singular value decomposition (K-SVD) [8]; and deep learning-based denoising algorithms, such as convolutional neural networks (CNN) [9], DnCNN [10], etc. Although existing denoising algorithms have achieved good results, they still have certain limitations.…”