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Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due...
In multi-view stereo setting, pixel correspondence problem and super resolution problem are inter-related in a sense that the result of each problem could help to solve the other. In this paper, we propose a novel method to solve two problems together by optimizing a unified energy functional. Main difference from the previous works is that the consistency between high resolution images is considered...
Handling motion blur is one of important issues in visual SLAM. For a fast-moving camera, motion blur is an unavoidable effect and it can degrade the results of localization and reconstruction severely. In this paper, we present a unified algorithm to handle motion blur for visual SLAM, including the blur-robust data association method and the fast deblurring method. In our framework, camera motion...
In this paper, we propose a new algorithm that solves both the stereo matching and the image denoising problem simultaneously for a pair of noisy stereo images. Most stereo algorithms employ L1 or L2 intensity error-based data costs in the MAP-MRF framework by assuming the naive intensity-constancy. These data costs make typical stereo algorithms suffer from the effect of noise severely. In this study,...
We propose a new vision-based SLAM (simultaneous localization and mapping) technique using both line and corner features as landmarks in the scene. The proposed SLAM algorithm uses an extended Kalman filter based framework to localize and reconstruct 3D line and corner landmarks at the same time and in real time. It provides more accurate localization and map building results than conventional corner...
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