This deals with the problem of recovering a high-resolution digital image from one low resolution digital image and proposes a super-resolution algorithm based on the mixed l0/l1 norm minimization. Introducing some assumptions and focusing the uniformity and the gradation of the image, this paper formulates the colorization problem as a mixed l0/l1 norm minimization and proposes the algorithm b...

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

In this paper, an iterative super-resolution reconstruction method is introduced for license plate recognition. A high-resolution image of the license plate is reconstructed by fusing the information derived from a set of subpixel shifted low-resolution images. The reconstruction problem is formulated as a system of linear equations that is solved by using the simultaneous algebraic reconstruct...

We present super-resolution methods that enhance image contrast and perform anisotropic diffusion simultaneously. Since we solve the super-resolution problem by encouraging Mach-band profiles while incorporating anisotropic diffusion, our technique not only reconstructs a high-resolution image from several overlapping noisy low-resolution images, but also enhances edges and image contrast while...

Super-resolution image reconstruction algorithms produce a high-resolution image from one or a set of low-resolution images of the desired scene. In this paper, we present a novel two-stage super-resolution (SR) algorithm combined sparse signal representation with the projection onto convex sets (POCS). In the first stage, inspired by recent results in sparse signal representation, we get a hig...

High resolution magnetic resonance (MR) imaging (MRI) is desirable in many clinical applications; however, there is a trade-off between resolution, speed of acquisition, and noise. It is common for MR images to have worse through-plane resolution (slice thickness) than in-plane resolution. In these MRI images, high frequency information in the through-plane direction is not acquired, and cannot...

A variety of super-resolution algorithms have been described in this book. Most of them are based on the same source of information however; that the super-resolution image should generate the lower resolution input images when appropriately warped and down-sampled to model image formation. (This information is usually incorporated into super-resolution algorithms in the form of reconstruction ...

In digital cameras, Bayer color filter array(CFA) images are captured and demosaicing is carried out before compression. Recently, it was found that compression-first schemes outperform the conventional demosaicing-first schemes in terms of output image quality. An efficient prediction-based lossless compression and super-resolution schemes are proposed in this paper. Compressed image is coded ...

We present a novel hybrid scheme called “hyper-resolution” that integrates image probability-filtering-based interpolation and improved Poisson maximum a posteriori (MAP) super-resolution to respectively enhance high spatial and spatial-frequency resolutions of a single down-sampled image. A new approach to interpolation is proposed for simultaneous image interpolation and smoothing by exploiti...

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. Simple combination of the two methods is problematic since the alignment used for mosaicing may not be a...