High spatial resolution is necessary for several applications such as visual inspection, and can be achieved using high-resolution image sensors or through image super-resolution (SR) algorithms. Currently, super-resolution algorithms are applied to either single low-resolution images or multiple low-resolution image sequences. In this paper, we propose a hybrid super-resolution (HYSR) framework to generate high-resolution images by combining multi-image super-resolution (MISR) and single-image super-resolution (SISR) to obtain high spatial resolution images. This method comprehensively utilizes sub-pixel-level high-frequency detail information between multiple images and co-occurrence prior of a single image to reconstruct SR images with a larger scale factor than the existing methods. Generally, the HYSR reconstruction results have more satisfactory details and visual quality than the SISR or MISR reconstruction results. A large number of qualitative and quantitive evaluation results demonstrate the effectiveness and superiority of the HYSR method over traditional MISR and SISR methods.
A calibration method for line-structured light (LSL) by using a virtual binocular vision system (VBVS) composed of one camera and a front coating plane mirror is promoted in this work. The front coating plane in the VBVS can generate much less coplanarity error in lithographic feature points and remarkably decline the imaging distortion during back coating. An encoded target is proposed to distinguish between real corners and virtual corners (mirrored corners) and achieve high-precision matching between real and virtual corners when the target is occluded during the VBVS calibration. A parameter optimization method based on 3D constraints is presented in the work to obtain accurate structural parameters and thus guarantee precise reconstruction of the LSL. Moreover, the laser stripe and its mirrored image meet the auto-epipolar constraint. Therefore, the matching between the real and virtual stripes can be realized based on the vanish point. The performance of our method is verified in the experiments.
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