A multiscale and multidirectional network named the Contourlet convolutional neural (CCNN) is proposed for synthetic aperture radar (SAR) image despeckling. SAR resolution not higher than that of optical images. If depth increased blindly, detail information flow will become quite weak, resulting in severe vanishing/exploding gradients. In this paper, a constructed, which single-stream structur...

This paper turns on ultrasound image despeckling by using Multiplicative Multiresolution Decomposition MMD. The proposed method is based on MMD’s coefficient thresholding. The used thresholds are automatically evaluated from the transformed image content. Tests are conducted on simulated speckled and real ultrasound images. Obtained results show that the proposed method outperforms the consider...

Speckle is a type of multiplicative noise that affects all coherent imaging modalities including Synthetic Aperture Radar (SAR) images. The presence speckle degrades the image quality and can adversely affect performance SAR applications such as automatic target recognition change detection. Thus, despeckling an important problem in remote sensing. In this paper, we introduce SAR-DDPM, denoisin...

The images captured from SAR sensors are inherently weakened by speckle noise. image processing community targeted this problem with many feature-based filters. Since low-contrast images, edge retention is the most crucial aspect to consider. This helps in efficient retrieval of information. paper provides a two-step edge-preserving homomorphic despeckling technique that implements guided filte...

wavelet shrinkage denoising approach is able to maintain local regularity of a signal while suppressing noise. However, the conventional wavelet shrinkage based methods are not timescale adaptive to track the local timescale variation. In this paper, a new type of Neural Shrinkage (NS) is presented with a new class of shrinkage architecture for speckle reduction in Synthetic Aperture Radar (SAR...