Yaniv Romano

Single Image Interpolation Via Adaptive Nonlocal Sparsity-Based Modeling

Single image interpolation is a central and extensively studied problem in image processing. A common approach toward the treatment of this problem in recent years is to divide the given image into overlapping patches and process each of them based on a model for natural image patches. Adaptive sparse representation modeling is one such promising image prior, which has been shown to be powerful in filling-in missing pixels in an image. Another force that such algorithms may use is the self-similarity that exists within natural images. Processing groups of related patches together exploits their correspondence, leading often times to improved results. In this paper, we propose a novel image interpolation method, which combines these two forces-nonlocal self-similarities and sparse representation modeling. The proposed method is contrasted with competitive and related algorithms, and demonstrated to achieve state-of-the-art results.

RAISR: Rapid and Accurate Image Super Resolution

Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the single image super-resolution problem. The idea is that with sufficient training data (corresponding pairs of low and high resolution images) we can learn set of filters (i.e., a mapping) that when applied to given image that is not in the training set, will produce a higher resolution version of it, where the learning is preferably low complexity. In our proposed approach, the run-time is more than one to two orders of magnitude faster than the best competing methods currently available, while producing results comparable or better than state-of-the-art. A closely related topic is image sharpening and contrast enhancement, i.e., improving the visual quality of a blurry image by amplifying the underlying details (a wide range of frequencies). Our approach additionally includes an extremely efficient way to produce an image that is significantly sharper than the input blurry one, without introducing artifacts, such as halos and noise amplification. We illustrate how this effective sharpening algorithm, in addition to being of independent interest, can be used as a preprocessing step to induce the learning of more effective upscaling filters with built-in sharpening and contrast enhancement effect.

The Little Engine That Could: Regularization by Denoising (RED)

Removal of noise from an image is an extensively studied problem in image processing. Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led some to believe that existing methods are touching the ceiling in terms of noise removal performance. Can we leverage this impressive achievement to treat other tasks in image processing? Recent work has answered this question positively, in the form of the Plug-and-Play Prior (

Boosting of Image Denoising Algorithms

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Improving K-SVD denoising by post-processing its method-noise

) method, showing that any inverse problem can be handled by sequentially applying image denoising steps. This relies heavily on the ADMM optimization technique in order to obtain this chained denoising interpretation. Is this the only way in which tasks in image processing can exploit the image denoising engine? In this paper we provide an alternative, more powerful, and more flexible framework for achieving the same goal. As opposed to the

Turning a denoiser into a super-resolver using plug and play priors

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Patch-disagreement as away to improve K-SVD denoising

method, we offer Regularization by Denoising (RED): using the denoising engine in defining the regulariza...

Con-Patch: When a Patch Meets Its Context

In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following “SOS” procedure: (i) Strengthen the signal by adding the previous denoised image to the degraded input image, (ii) Operate the denoising method on the strengthened image, and (iii) Subtract the previous denoised image from the restored signal-strengthened outcome. The convergence of this process is studied for the K-SVD image denoising and related algorithms. Still in the context of K-SVD image denoising, we introduce an interesting interpretation of the SOS algorithm as a technique for closing the gap between the local patch-modeling and the global restoration task, thereby leading to improved performance. In a quest for the theoretical origin of the SOS algorithm, we provide a graph-based interpretation of our method, where the SOS recursive update effectively minimizes a penalty function that aims to denoise the image, while being regularized...

On the Global-Local Dichotomy in Sparsity Modeling

Various patch-based image denoising algorithms have been shown to be very effective. Nevertheless, in most cases the difference between the noisy image and its denoised version (called “method-noise”) still contains traces of the original image content. In this paper we propose a novel technique for improving the K-SVD denoising results. Our scheme starts by applying the K-SVD on the given noisy image. Then, for each patch, we recover the “stolen” image content information from the method-noise by performing iterations of de-noising using the same atoms that represent the first-stage de-noised patch. Experimental results demonstrate the efficiency of this technique.

Dynamical system classification with diffusion embedding for ECG-based person identification

Denoising and Super-Resolution are two inverse problems that have been extensively studied. Over the years, these two tasks were treated as two distinct problems that deserve a different algorithmic solution. In this paper we wish to exploit the recently introduced Plug-and-Play Prior (PPP) approach to connect between the two. Using the PPP, we turn leading denoisers into super-resolution solvers. As a case-study we demonstrate this on the NCSR algorithm, which has two variants: one for denoising and one for superresolution. We show that by using the NCSR denoiser, one can get equal or even better results when compared with the NCSR super-resolution.