A new super-resolution method for monochrome camera array imaging

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.