We give a method for improving the resolution of surfaces captured with a laser range scanner by combining many very similar scans. This idea is an application of the 2D image processing technique known as superresolution. The input lower-resolution scans are each randomly shifted, so that each one contributes slightly different information to the final model. Noise is reduced by averaging the ...

The combination of image mosaicing and super-resolution imaging, i.e. superresolution mosaicing, is a powerful means of representing all the information of multiple overlapping images to obtain a high resolution broad view of a scene. In most current image acquisition systems, images are routinely compressed prior to transmission and storage. In this paper, we present a robust super-resolution ...

In this paper a novel technique for Neighbor embedding single image super resolution (SR) is proposed. Given a low-resolution image, its highresolution image is reconstructed from a set of training images, which are composed of one or more lowresolution and corresponding high-resolution image pairs. In this paper we propose a new approach to a single image super-resolution through neighbor embe...

Existing approaches to super-resolution are not applicable to videos of faces because faces are non-planar, non-rigid, non-lambertian, and are subject to self occlusion. We present super-resolution optical ow as a solution to these problems. Super-resolution optical ow takes as input a conventional video stream, and simultaneously computes both optical ow and a super-resolution version of the e...

Image super resolution concept has been introduced for image enhancement in various applications. Image enhancement is crucial operation essential for reducing different possible degradations of the captured image. More sophisticated techniques are already proposed. Wavelet transform based algorithms are widely used in many applications. Wavelet transform/s is used to extrapolate missing high f...

In this paper, we propose a framework to detect human activities by constructing super-resolution images from the MODIS data. The highest resolution of the MODIS images is 250 meters per pixel, which is usually not enough to detect human activities. By magnifying and de-blurring the low resolution MODIS image through the SupportVector Regression, the constructed super-resolution image can achie...

In the literature there has been much research into two methods of attacking the super-resolution problem: using optical flow-based techniques to align low-resolution images as samples of a target high-resolution image, and using learning-based techniques to estimate perceptuallyplausible high frequency components of a low-resolution image. Both of these approaches have been naturally extended ...

Learning based super-resolution (SR) methods, which predict the high-resolution pixel values but not directly provide an estimation of uncertainty, are typically non-probabilistic and have limited generalization ability. Gaussian processes can provide a framework for deriving regression techniques with explicit uncertainty models, but Gaussian Process Regression (GPR) has a significant drawback...

Recently, super-resolution reconstruction (SRR) method of low-dimensional face subspaces has been proposed for face recognition. This face subspace, also known as eigenface, is extracted using principal component analysis (PCA). One of the disadvantages of the reconstructed features obtained from the super-resolution face subspace is that no class information is included. To remedy the mentione...