Yaping Huang

Image deblurring with filters learned by extreme learning machine

Abstract Image deblurring is a basic and important task of image processing. Traditional filtering based image deblurring methods, e.g. enhancement filters, partial differential equation (PDE) and etc., are limited by the hypothesis that natural images and noise are with low and high frequency terms, respectively. Noise removal and edge protection are always the dilemma for traditional models. In this paper, we study image deblurring problem from a brand new perspective—classification. And we also generalize the traditional PDE model to a more general case, using the theories of calculus of variations. Furthermore, inspired by the theories of approximation of functions, we transform the operator-learning problem into a coefficient-learning problem by means of selecting a group of basis, and build a filter-learning model. Based on extreme learning machine (ELM) [1] , [2] , [3] , [4] , an algorithm is designed and a group of filters are learned effectively. Then a generalized image deblurring model, learned filtering PDE (LF-PDE), is built. The experiments verify the effectiveness of our models and the corresponding learned filters. It is shown that our model can overcome many drawbacks of the traditional models and achieve much better results.

Railroad online: acquiring and visualizing route panoramas of rail scenes

A patrol type of surveillance has been performed everywhere from police city patrol to railway inspection. Different from static cameras or sensors distributed in a space, such surveillance has its benefits of low cost, long distance, and efficiency in detecting infrequent changes. However, the challenges are how to archive daily recorded videos in the limited storage space and how to build a visual representation for quick and convenient access to the archived videos. We tackle the problems by acquiring and visualizing route panoramas of rail scenes. We analyze the relation between train motion and the video sampling and the constraints such as resolution, motion blur and stationary blur etc. to obtain a desirable panoramic image. The route panorama generated is a continuous image with complete and non-redundant scene coverage and compact data size, which can be easily streamed over the network for fast access, maneuver, and automatic retrieval in railway environment monitoring. Then, we visualize the railway scene based on the route panorama rendering for interactive navigation, inspection, and scene indexing.

Route panorama acquisition and rendering for high-speed railway monitoring

With the rapid development of high-speed railway, the safety running of trains becomes extremely urgent. Video is a direct and effective manner for railway environment monitoring. In this paper, we propose a route panoramic representation to produce a virtual environment of the railway scene from a train-borne video, which provides an all-round display for the entire length of railway. We generate the route panorama from the forward motion video and then render 3D scene. The experimental results show many advantages of the representation in the large data storage, browsing, and examination. It will be used for railway safety checking, railway facility inspection and virtual sightseeing from the train in the future.

Monocular stereo imaging using bi-slit projections from Forward motion video

The video captured from forward moving camera has been used for environment surveillance, endoscopy, pipeline inspection and mobile navigation, from which 3D information acquisition will further facilitate the applications. In this paper, we proposed a new method named bi-slit projections to achieve the stereo imaging from forward motion video captured by only single camera. The method only requires simple geometrical calculation without computation of any 3D structure. The epipolar geometry about parallax and baseline also be formulated in detail. The experimental results including indoor and outdoor scene demonstrate the effectiveness of our proposed method by generating the stereoscopic 3D image and calculating the lateral depth map.

Learning Topographic Representations of Nature Images with Pairwise Cumulant

In this paper, we propose a model for natural images to learn topographic representations and complex cell properties. Different from the estimation of traditional models, e.g., pooling the outputs of filters in neighboring regions, our method maximizes a simple form of binary relations between two adjacent complex cells—“pairwise cumulant”, which contains the favorable nonlinearity as high order cumulant, and can exploit the “sparseness” and “correlation” of cells in primary visual cortex. By means of choosing nonlinearity properly, our model is related to cumulant-based ICA model, and the derived fixed-point algorithm is close to the well-known FastICA algorithm. The local convergence analysis proves that our fixed-point algorithm is cubic convergence and experiments on nature images show its high efficiency than traditional algorithms. Besides, simulations demonstrate the effectiveness of our model in capturing nonlinear dependencies among these neighboring complex cells. The learnt filters preserve properties of complex cells, and their orientation, spatial frequency and location change smoothly over the topographic map. In addition, these learnt filters can be used as feature descriptors. They produce features that are invariant to object transformations, and achieve better results than traditional models on digit recognition tasks.

A regularized restoration model based on geometrical features and noise evaluation

In this paper, we propose a new method for designing the variation restoration model which uses the noise evaluation to decide the approximation term and the information of geometrical structures in the blurred and noised images to choose the regularization term. We adjust the measurement for the approximation term based on the noise variance in the degraded image. By computing the mean curvature which is a local geometrical feature of a image surface, we can use the geometrical information to determine the regularization term effectively. This kind of regularization term can obtain a better proportion between de-noising and keeping edges and texture while avoiding piecewise constant because this model diffuses anisotropic. And it is a restoration model that can adjust the measurement adaptively according to the degraded image instead of using the single measurement to restore all different images. In addition, by using the inherent geometric features, we do not need to take any laborious work to choose an energy functional any more. Our experiments show that our idea is on the correct way and our method can preserve the details of the image while removing noises.

Rendering Railway Scenes in Cyberspace Based on Route Panoramas

The high demand from railway safety has promoted the sensing technology to avoid manual inspection that is error-prone, low efficiency, labor-intensive and high costs. However, few works have emphasized railway environmental monitoring so far which is equally important in railway safety maintenance. In this paper, we propose a railway scene rendering system for virtual maneuver and inspection. Based on the route panoramas extracted from a real train-borne video, we generate the compact and high fidelity scenes over a long distance route in cyberspace. We analyze the relation between train motion and the video sampling in order to obtain the route panoramas of the railways at the just-sampling rate. Then, we render scene tunnels based on the route panorama in a virtual space for interactive navigation, inspection, and scene indexing. The experimental results show remarkable advantages in the data storage and automatic inspection. The compact data format with sufficient visual information can be transmitted and released in cyberspace for fast access, maneuver, and automatic retrieval in railway environment monitoring.