2 research outputs found
Nonlocal Co-occurrence for Image Downscaling
Image downscaling is one of the widely used operations in image processing
and computer graphics. It was recently demonstrated in the literature that
kernel-based convolutional filters could be modified to develop efficient image
downscaling algorithms. In this work, we present a new downscaling technique
which is based on kernel-based image filtering concept. We propose to use
pairwise co-occurrence similarity of the pixelpairs as the range kernel
similarity in the filtering operation. The co-occurrence of the pixel-pair is
learned directly from the input image. This co-occurrence learning is performed
in a neighborhood based fashion all over the image. The proposed method can
preserve the high-frequency structures, which were present in the input image,
into the downscaled image. The resulting images retain visually important
details and do not suffer from edge-blurring artifact. We demonstrate the
effectiveness of our proposed approach with extensive experiments on a large
number of images downscaled with various downscaling factors.Comment: 9 pages, 8 figure
Linearized ADMM and FAST nonlocal denoising for efficient plug-and-play restoration
In plug-and-play image restoration, the regularization is performed using powerful denoisers such as nonlocal means (NLM) or BM3D. This is done within the framework of alternating direction method of multipliers (ADMM), where the regularization step is formally replaced by an off-the-shelf denoiser. Each plug-and-play iteration involves the inversion of the forward model followed by a denoising step. In this paper, we present a couple of ideas for improving the efficiency of the inversion and denoising steps. First, we propose to use linearized ADMM, which generally allows us to perform the inversion at a lower cost than standard ADMM. Moreover, we can easily incorporate hard constraints into the optimization framework as a result. Second, we develop a fast algorithm for doubly stochastic NLM, originally proposed by Sreehari et al. (IEEE TCI, 2016), which is about 80� faster than brute-force computation. This particular denoiser can be expressed as the proximal map of a convex regularizer and, as a consequence, we can guarantee convergence for linearized plug-and-play ADMM. We demonstrate the effectiveness of our proposals for super-resolution and single-photon imaging. © 2018 IEEE