Fundamental limits of reconstruction-based superresolution were proposed in [1]. In this note, we point out that their proof is incomplete.

Super-resolution deals with the construction of highresolution images using a set of low-resolution images obtained from a scene with subpixel shifts. These lowresolution images are typically obtained from a jittery camera source, such as a camera mounted on a vibrating aircraft, or a slowly-moving subject, such as a few frames of a standing person in front of a surveillance camera. The low-res...

Image super-resolution is the task of obtaining a high-resolution (HR) image of a scene given low-resolution (LR) image(s) of the scene. In this project, we have focused on the task of super-resolution given a single LR image, which is usually the case. There exist many techniques in literature addressing this task, and we have considered two techniques having the essence of [1] and [2]. In fir...

The Problem: Pixel representations for images do not have resolution independence. When we zoom into a bitmapped image, we get a blurred image. Figure 1 shows the problem for a teapot image, rich with real-world detail. We know the teapot’s features should remain sharp as we zoom in on them, yet standard pixel interpolation methods, such as pixel replication (b, c) and cubic spline interpolatio...

The extraction of a single high-quality image from a set of low-resolution images is an important problem which arises in fields such as remote sensing, surveillance, medical imaging and the extraction of still images from video. Typical approaches are based on the use of cross-correlation to register the images followed by the inversion of the transformation from the unknown high resolution im...

Mosaicing and super resolution are two ways to combine information from multiple frames in video sequences. Mosaicing displays the information of multiple frames in a single panoramic image. Super-resolution uses regions which appear in multiple frames to improve resolution and reduce noise. Combining both methods enables to represent all the static information in the sequence in a compact and ...

In this paper we propose a vision system that performs image Super Resolution (SR) with selectivity. Conventional SR techniques, either by multi-image fusion or example-based construction, have failed to capitalize on the intrinsic structural and semantic context in the image, and performed “blind” resolution recovery to the entire image area. By comparison, we advocate examplebased selective S...

This paper introduces two algorithms for enhancing image resolution from an image sequence. The “image-based” approach presumes that the images were taken under the same illumination conditions and uses the intensity information provided by the image sequence to construct the high-resolution image. This ideal, however, is almost always not true when the illumination varies. The “edge-based” app...

We describe a novel method for blind, single-image spectral super-resolution. While conventional superresolution aims to increase the spatial resolution of an input image, our goal is to spectrally enhance the input, i.e., generate an image with the same spatial resolution, but a greatly increased number of narrow (hyper-spectral) wavelength bands. Just like the spatial statistics of natural im...