Noise reduction is a critical research area in current remote sensing image processing. Existing denoising techniques for remote sensing images often encounter challenges such as blurred edges and excessive smoothing. To overcome these limitations, we propose a novel hierarchical denoising model based on an Autoencoder. Our model effectively addresses the issue of distinguishing low-rank residuals and preserving essential details in remote sensing images, while also extracting edge features from the residuals with high efficiency. To validate the effectiveness of our approach, we conduct comprehensive experimental tests on a representative remote sensing dataset. The results demonstrate that our method successfully preserves edge details while achieving superior denoising performance compared to state-of-the-art techniques.

Synthetic aperture radar (SAR) image despeckling is a challenging task as speckle noise is spatially correlated and signal-dependent, and appears as a grainy texture superimposed on images. Although traditional low-rank SAR image despeckling methods have shown promising performance, they have the problem of producing over-smoothed images with blurred edges due to their low-rank characteristics. In this paper, we propose a novel edge preserved SAR despeckling method named EP-LRSID, which can keep rich edge details while reducing speckle noise. Specifically, EP-LRSID takes a fresh look at the low-rank model, i.e., we can obtain structural edge information from residuals which is viewed as noise and simply disregarded by the traditional low-rank methods. To obtain discriminative edge information from residuals, the edge subspace is obtained in a manifold framework by using a dynamic affinity graph regularization. Moreover, a new hierarchical prior knowledge regulation is designed to make different kinds of pixels processed hierarchically, especially the strong scattering points in SAR images. By introducing this prior knowledge, our low-rank model can obtain more confidential low-rank parts and edge parts, thus structural information including edges can be better preserved in this way. Extensive experiments on several real and synthetic datasets demonstrate that EP-LRSID can achieve the highest despeckling performance with edge preservation than other state-of-the-art despeckling algorithms.

Detecting humans in images is a useful application of computer vision. Loose and textured clothing, occlusion and scene clutter make it a difficult problem because bottom-up segmentation and grouping do not always work. We address the problem of detecting humans from their motion pattern in monocular image sequences; extraneous motions and occlusion may be present. We assume that we may not rely on segmentation or grouping and that the vision front-end is limited to observing the motion of key points and textured patches in between pairs of frames. We do not assume that we are able to track features for more than two frames. Our method is based on learning an approximate probabilistic model of the joint position and velocity of different body features. Detection is performed by hypothesis testing on the maximum a posteriori estimate of the pose and motion of the body. Our experiments on a dozen of walking sequences indicate that our algorithm is accurate and efficient.

We propose an EM approach combined with the modified separation matrix scheme to perform 3D motion segmentation of the image sequence which contains multiple moving objects. We observe that, given the detected features and their 2D optical flow, in most cases the objects or their flow are separated very well from each other in space. The separation matrix method modified by using normalized cuts achieves expected grouping results for these cases. However, when the objects are overlapped spatially but undergoing independent motions, such as Ullman's co-axial transparent cylinder demonstration, there will be no proper affinity to perform segmentation by that way. Pure underlying 3D motion becomes the only cite to segment the scene. We exploit the EM algorithm to deal with such difficult cases. The scheme is tested on vast number of synthetic image sequences. Results with real image sequence are also given.