The state-of-the-art blind image deblurring (BID) methods are sensitive to noise, and most of them can deal with only small levels of Gaussian noise. In this paper, we use simple filters to present a robust BID framework which is able to robustify exiting BID methods to high-level Gaussian noise or/and Non-Gaussian noise. Experiments on images in presence of Gaussian noise, impulse noise (salt-and-pepper noise and random-valued noise) and mixed Gaussian-impulse noise, and a real-world blurry and noisy image show that the proposed method can faster estimate sharper kernels and better images, than that obtained by other methods.
Our paper presents a method for reconstructing a high-resolution (HR) image from a set of multi-view color images captured by a camera array. First, an accurate depth map of low-resolution (LR) image captured by a selected reference camera is obtained using graph cuts. Then, a HR image corresponding to the reference camera can be estimated by super-resolution reconstruction. Experiments on real images show the effectiveness of our method.
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