Striping effects are common phenomena in remote sensing images, and they significantly limit subsequent applications. Although many destriping approaches have been developed, there aren’t many that can completely eliminate complex stripes with varying levels of strength. To address this issue, we propose a stripe removal model based on a variable weight coefficient and group sparse regularization. Specifically, rather than a single scalar for the stripes in most approaches, different weights are set for different stripe rows to estimate the stripes with varying intensities. An adaptive method to estimate the weight matrix is proposed. On the other hand, group sparsity regularization is employed to constrain the entire stripe. In addition, region weights are designed for regions with different stripe characteristics. The alternating direction multiplier method is employed to solve the proposed model by alternating minimization. Experimental results based on simulation and real data demonstrate that the proposed model outperforms other advanced methods in terms of stripe noise removal and image detail preservation.
Despite that 3D human body reconstruction from a single image has obtained rapid progress in recent years, most methods aim at the body without the hands and face. However, hand gestures and facial expressions are also important for delivering human intentions or emotions. This paper proposes a method for holistic 3D reconstruction of the human body from a single RGB image, including hands, body, and face. Our approach is based on the SMPL eXpressive (SMPL-X), a unified 3D parametric human body model of body, hands, and face. Since it is difficult to exactly regress the model's parameters of different body parts by a single framework, we use a divide-and-conquer strategy for the whole human body reconstruction. We exploit different deep neural networks to predict the hand, body, and head model's parameters, then integrate them into an entire 3D model to realize a holistic and expressive 3D human body reconstruction. Simulation results demonstrate that our method has obtained state-of-the-art performance with better facial expression.
Pantograph carbon slide is an important device in power supply system of electric locomotive, the pantograph location is greatly significant for the geometric parameter measurement of the pantograph-catenary system. In order to enhance the adaptability of pantograph detection algorithm to the scene, and to reduce the false rate and missing rate of pantograph detection, this paper proposes a novel method based on the pantograph template for fast matching and horizontal edge detection projection in monocular infrared pantograph images. Firstly, the prior knowledge of the position of the pantograph and the catenary is combined with the template matching method to realize the rough location of the pantograph, and then the precise location of the pantograph by horizontal edge detection and horizontal unilateral projection. The experimental results show that this novel adaptive method realizes the non-contact detection and location of the pantograph effectively, and improve the efficiency significantly.
The extraction of Region of Interest (ROI) is an important information guarantee in the application of imaging matching guidance, which directly affects the acquisition probability and matching accuracy of the target. Image segmentation is an important method to extract the Region of Interest of the target. Based on image segmentation algorithm, histogram equalization and morphological filtering, this paper proposes an effective image processing method to extract the Region of Interest of the target. (1) A variety of image threshold segmentation methods are applied to the actual processing flow, and their segmentation performance is compared and analyzed. Some image segmentation methods are obtained, which are suitable for target region extraction in template image preparation and target potential region location in matching recognition. (2) Preliminary localization of visible remote sensing images is carried, using color information, to obtain local regions, then enhance the image using histogram equalization method, finally morphological filtering is used to remove the edge noise. (3) The Otsu method and Kittler minimum error method are processed in parallel, then the segmentation results are fused, and the evaluation indexes such as area constraint, similarity and contrast are filtered to obtain the target region .Tests have been done with visible image and infrared image in this paper. The result indicates that the effectiveness of the morphological filter is more obvious after histogram equalization for the original image. Besides, the Otsu method and Kittler minimum error method are processed in parallel, then the segmentation results are fused to get a more precise Region of Interest, thus ensuring the accuracy and timeliness of imaging matching guidance.
X-ray angiograms, which suffer from low-contrast and noise, need to be improved by both the image enhancement and denoising techniques. However, the goals of these two tasks usually conflict, which makes it difficult to efficiently combine the enhancement and denoising in one scheme. To solve this problem, we propose a novel spatial-frequency filtering (SFF) scheme to simultaneously enhance and denoise low-quality X-ray cardiovascular angiogram images. The proposed scheme includes three key components: Firstly, a relative total variation method is employed as a guide filter to separate an input image into two parts, including the base layer with strong structures and the detail layer with weak structures and noise. Then the base layer is enhanced by a proposed improved histogram equalization (IHE) method while the detail layer is extracted by a short-time Fourier transform and is further enhanced by using a proposed adaptive correction parameter. Finally, the improved image is the combination of results obtained by the two components. Both quantitative and qualitative results of experiments on real-world low-quality X-ray angiogram images demonstrate that the proposed method outperforms the state-of-the-arts in terms of contrast enhancement, structure preservation, and noise reduction.
The pantograph-catenary interaction is a safety indicator of electric multiple unit (EMU) trains. It is important for EMU trains to monitor the contact-point of pantograph and catenary in real time. In this paper, a highly efficient contactpoint- tracking approach, called horizontal-vertical enhancement and tracking method (HEATM), is proposed to enhance infrared images which are used to detect and track contact-point. The HEATM includes the following three key components. Firstly, the HEATM separates the input infrared image into the horizontal images (HIs) layer and vertical images (VIs) layer. Secondly, the contact-wire tracking model is updated by the points from HIs while the pantographtracking model is updated by the points from VIs. Finally, the key contact-points are positioned by the tracking model and continuously analyzed to obtain robust tracking signals. The quantitative and qualitative results validate the effectiveness of the proposed scheme. Moreover, our method presents very robust and efficient for contact-point detecting and tracking at a speed of 70 fps and 97.8% average accuracy in two datasets (12000 frames), which is very beneficial for its extensive application.
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