In recent years, dictionaries combined with sparse learning techniques became extremely popular in computer vision. The image denoising approaches can be categorized as spatial domain, transform domain, and dictionary learning based according to the image representation. Using machine learning, sparse representations have become a trend and are used image and vision applications. The general id...

This paper presents two approaches for filter design based on stochastic distances for intensity speckle reduction. A window is defined around each pixel, overlapping samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The tests stem from stochastic divergences within the Information Theory framework. The technique is applied to intensit...

Ultrasound is a medical diagnostic technique that is widely used because of its low cost and capability of forming real time imaging. A major problem regarding ultrasound images is the corruption by speckle noise. In this paper, speckle noise is removed by the methods based on Hard Thresholding based on Curvelets and Spatial filtering techniques such as Sobel operator, prewitt operator, Lee fil...

Radar and Landsat data were used to classify land cover in north central Ethiopia. Both images were registered and resampled to 12.5 m spatial resolution. Maximum Likelihood Classifier (MLC) and C4.5 algorithm were applied. The original radar data produced low overall classification accuracy (66%). To improve this classification accuracy, de-speckling and texture measures were used for image en...

Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based and non-local filtering methods. However, SAR images usually contain different types of regions, homogeneous heterogeneous regions. Some filters co...