Medical ultrasound images are usually corrupted by the noise during their acquisition known as speckle. Speckle noise removal is a key stage in medical ultrasound image processing. Due to the ill-posed feature of image denoising, many regularization methods have been proved effective. This paper introduces an approach which collaborate both sparse dictionary learning and regularization method to remove the speckle noise. The method trains a redundant dictionary by an efficient dictionary learning algorithm, and then uses it in an image prior regularization model to obtain the recovered image. Experimental results demonstrate that the proposed model has enhanced performance both in despeckling and texture-preserving of medical ultrasound images compared to some popular methods.
Remote sensing image enhancement algorithm, which can enhance the contrast of image without changing its information, is crucial for the acquisition and analysis of remote sensing information. In this paper, we compare the performance of several state-of-the-art enhancement algorithms in processing remote sensing images. We classify and refine the main idea of each algorithm, and evaluate the enhancement effect with subjective evaluation and objective metrics such as entropy, brightness, contrast and mean gradient. In order to avoid color-enhanced interference, this paper took experiments both on RGB pictures and grayscale pictures. The significance and compared results were shown in the following paper.