Multi-view super-resolution refers to the process of reconstructing a high-resolution image from a set of low-resolution images captured from different viewpoints typically by different cameras. These multi-view images are usually obtained by an array of the same color cameras. However, the color cameras have color filter array to acquire color information, which reduces the quality of obtained images. To avoid color camera, and obtain higher resolution color images, we do research on a camera array which consists of interlaced different monochrome cameras and propose a new super-resolution method based on the camera array. Given that MVSR is an ill-posed problem and is typically computationally costly, we super-resolve multi-view monochrome images of the original scene via solve a regularization optimization problem consisting of a data-fitting term and three regularization terms on image, blur and cross-channel priors. The resulting optimization problems with respect to the desired image and with respect to the unknown blur are efficiently addressed by the alternating direction method of multiplier. Corresponding experimental results, conducted on a series of datasets captured by our own camera array system, demonstrate the effectiveness of the proposed method.
The technology of binocular camera matures day by day. Compared with monocular camera, it can obtain higher resolution images at a lower cost than monocular cameras. However, existing high dynamic range methods based on images acquired by monocular camera, causing the result images to be noisy and blurry. In order to solve the problem, this paper presents a new high dynamic range method based on monochrome-color camera system. We first use the camera system to obtain multiple sets of different exposure monochrome-color image pairs, and then match the same exposure image pair. By using the color propagation methods, we combine the color information from color image with detail information from monochrome image, and obtain multiple sets of different exposures, sharper, low-noise images with more details. And finally get the result through high dynamic imaging and tone mapping. Experiments show that our method is better than the results of the classical method.
High dynamic range (HDR) images can show more details and luminance information in general display device than low dynamic image (LDR) images. We present a robust HDR imaging system which can deal with blurry LDR images, overcoming the limitations of most existing HDR methods. Experiments on real images show the effectiveness and competitiveness of the proposed method.
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