Texture synthesis has proven successful at imitating a wide variety of textures. Adding additional constraints (in the form of a low-resolution version of the texture to be synthesized) makes it possible to use texture synthesis methods for texture superresolution. The Single-image Superresolution Problem The problem we are trying to solve is the following: Given: a low-resolution image and hig...

This paper addresses the problem of single image super-resolution (SR), which consists of recovering a high resolution image from its blurred, decimated and noisy version. The existing algorithms for single image SR use different strategies to handle the decimation and blurring operators. In addition to the traditional first-order gradient methods, recent techniques investigate splitting-based ...

This paper develops a multi-frame image super-resolution approach from a Bayesian view-point by marginalizing over the unknown registration parameters relating the set of input low-resolution views. In Tipping and Bishop’s Bayesian image super-resolution approach [16], the marginalization was over the superresolution image, necessitating the use of an unfavorable image prior. By integrating ove...

A global robust M-estimation scheme for maximum a posteriori (MAP) image super-resolution, which efficiently addresses the presence of outliers in the low resolution images is proposed in this work. In iterative MAP image super-resolution, the objective function to be minimized involves the highly resolved image, a parameter controlling the step size of the iterative algorithm and a parameter w...

This paper presents a novel technique to simultaneously estimate the depth and the focused image of a scene both at a super-resolution, from its defocused observations. Super-resolution refers to the generation of high resolution images from a sequence of low resolution images. Hitherto, the super-resolution technique has been restricted only to the intensity domain. In this paper, we extend th...

-This paper introduces a new examplar-based inpainting framework. A coarse version of the input image is first inpainted by a non-parametric patch sampling. Compared to existing approaches, some improvements have been done (e.g. filling order computation, combination of K nearest neighbours). The inpainted of a coarse version of the input image allows to reduce the computational complexity, to ...

Multiframe super-resolution algorithms can be used to reconstruct a high-quality high-resolution image from several warped, blurred, undersampled, and possibly noisy images. A widely used means of implementing such algorithms is by optimization-based model inversion. In the past, steepest-descent methods have been applied. While easy to implement, these methods are known for their poor converge...

Single image super-resolution task aims to reconstruct a high-resolution from low-resolution image. Recently, it has been shown that by using deep prior (DIP), single neural network is sufficient capture low-level statistics only without data-driven training such can be used for various restoration problems. However, tasks are difficult perform with DIP when the target noisy. The super-resolved...

From a set of shifted, blurred, and decimated image , super-resolution image reconstruction can get a high-resolution image. So it has become an active research branch in the field of image restoration. . In general, super-resolution image restoration is an ill-posed problem. Prior knowledge about the image can be combined to make the problem well-posed, which contributes to some regularization...

Example-based super-resolution (EBSR) [3, 4] reconstructs a highresolution image from a low-resolution image, given a training set of high-resolution images. In this note I propose some applications of EBSR to medical imaging. A particular interesting application, which I call x-ray voxelization, approximates the result of a CT scan from an x-ray image. 1 Example-based super-resolution Example-...