![]() ![]() Subsequently, an exposure restoration network is constructed to recover the multi-exposure information of the registered non-overlapping regions, which is then linearly fused with the previous registered results to generate the stitched wide-FOV multi-exposure LF images. ![]() Specifically, the proposed method first exploits tensor decomposition to obtain a compact representation of high-dimensional LF image, so as to enable a computationally efficient 2D neural network for LF registration. To address these problems, this paper proposes an unsupervised wide-FOV high dynamic range (HDR) LF imaging method, which can effectively reconstruct a wide-FOV HDR LF image from a set of source LF images captured from different perspectives and simultaneously with different exposures. However, limited by the optical structure of the LF camera, the acquired LF images usually suffer from narrow field of view (FOV) and low dynamic range. Light field (LF) imaging, which simultaneously captures the intensity and direction information of light rays, enabling many vision applications, has received widespread attention. Experimental results show that our extendable networks can well balance the accuracy and inference speed, and the sizes of all models are less than 9MB. In the case of sufficient computational resources, this basic network can also be extended to a recurrent coarse-to-fine form to achieve the most accurate results. To further deal with the large displacements, we extend the basic network to a multiscale weight-shared form to additionally process the half-scale input. In this paper, we build a basic network based on the ShuffleNetV2 compressed units, which can extremely accelerate the homography estimation process. However, most existing methods have large model sizes and low inference speed, which make them infeasible in terminal devices and real-time scenarios. Recently, learning-based methods have been proposed to use semantic information to solve challenging cases like large displacements, dynamic scenes, and illumination changes, where traditional methods may degrade. Fast and accurate homography estimation between images is crucial for relative pose estimation in autonomous exploration. ![]()
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