Speckle Suppression Method in SAR image Based on Curvelet Domain BivaShrink Model

Abstract

<p class="Abstract">Based on the statistical property of SAR image speckle noise and the property that the multiscale geometric analysis can capture the intrinsic geometrical structure of image, combining curvelet transform with BivaShrink denoising model, a method of SAR image denoising based on curvelet domain is presented in this paper. According to calculation of variance homogeneous measurement and curvelet coefficients of current layer and its parent layer, the local adaptive window is determined to optimally estimate shrinkage factor. The method can effectively reduce SAR speckle noise and preserving details of SAR image well through the correlation of curvelet coefficients in the same direction of subband and parent-layer subband. The experimental results show the presented method greatly improves the subjective visual effect and the numerical indicators of the denoised image.</p

Speckle suppression in SAR images employing modified anisotropic diffusion filtering in wavelet domain for environment monitoring

“…The methodology owing to its versatility could be effectively applied for environment monitoring. Gupta et al, [37], (d) L. Torres et al [40], (e) W. Wang et al [41].…”

“…Additionally, for the purpose of comparisons, the results with other speckle suppression techniques such as those of W. Wang et al [41], L. Torres et al [40], and A. Gupta et al [37] are also included and shown in Figure 3 for both medium as well as high speckled images. The results of Wang et al [41] are satisfactory in terms of speckle suppression for medium levels of speckle content but considerate amount of residual noise is still available at high levels of speckle. Also, the presence of blurred and over despeckled edges in the image is a major limitation of this approach.…”

An Adaptive SAR Despeckling Method Using Cuckoo Search Algorithm

“…In contrast, the curvelet transform is a better noise reduction scheme which is designed using mutilscale ridgelets at very fine scales to represent the curved edges as straight lines. This property of curvelet transform help preserve edges in a noisy image [9][10][11]. However, they fail to smooth homogeneous areas.…”

A Novel Variational Model with Strict Convexity for Multiplicative Noise Removal

“…The additive noise is the most widely considered noise in the literature, which has been extensively studied over the last decades, and tends to be quite comprehensive and mature [5]- [7]. In many real world image processing applications, multiplicative noises are commonly found, for example in laser images, microscope images, synthetic aperture radar (SAR) images and medical ultrasonic images [8], [9]. How to remove the multiplicative noise in the corrupted images is becoming the hot research issue in recent years [10]- [13].…”

“…Several variational approaches for multiplicative noise removal problems are available in the literature [8][9][10][11][12]. The variational approach dealing with multiplicative noise was firstly proposed by Rudin, Lions and Osher in [15] (called the RLO model).…”