cr1265 - Deblurring And Denoising with Edge Enhancement of...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
Deblurring And Denoising with Edge Enhancement of Satellite Images Using Super Resolution Techniques A. Cheref and C. Serief National Center of Space Techniques BP 13 Arzew 31200 Oran, Algeria e-mail: cheref_am, [email protected] Abstract In this paper we propose two algorithms of super resolution techniques. We introduce the Iterative Back Projection (IBP) algorithm in the case of deblurring images and second algorithm consist an edge-enhancing super- resolution algorithm using anisotropic diffusion technique. Because we solve the super-resolution problem by incorporating anisotropic diffusion and IBP, these techniques does more than merely reconstruct a high-resolution image from several overlapping blurred and noisy low resolution images and preserve them. In addition to deblurring and reducing image noise during the restoration process, these methods also enhances edges. We apply this technique to the Alsat-1 images. S I. INTRODUCTION INCE the advance of earth observation satellites in the late 70's, remotely sensed imagery has been widely used in many fields of earth science. Due to its limited ground resolution, applications in large-scale mapping, planning, zoning and evaluation are not yet readily for business purposes. Image sharpness is an important parameter in signal processing. Indeed, interpreting an image is determined by the possibility of extracting the information it contains. The sharper an image is, the more details it shows. If it is blurred, it will be hard to interpret it. Thus we are limited by the resolution of the image. Image resolution depends on the physical characteristics of the sensor: the optics and the density and spatial response of the detector elements. Increasing the resolution by sensor modification may not be an available option. The alternative approach is to use image processing methods to construct a high resolution image, if multiple (possibly degraded), shifted, low resolution images are available. Recently, such a resolution enhancement approach has been one of the most active research areas, and it is called Super Resolution (SR) (or HR) image reconstruction. In this paper we address the problem of enhancing remote sensing images using super resolution technique. II. SUPER-RESOLUTION Super-resolution is the process of reconstructing a high resolution image from low-resolution input ones. It received much attention in computer vision and image processing communities over the past decades [1, 2, 3]. Super- resolution algorithms take advantage of overlapping regions which appear in multiple frames. Using the image displacements, which should be computed with subpixel accuracy, the multiple overlapping frames provide a dense sampling, and thus enable restoring high frequencies and improve resolution.
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 04/22/2010 for the course MI IP taught by Professor Vladbalan during the Spring '10 term at Universidad del Rosario.

Page1 / 4

cr1265 - Deblurring And Denoising with Edge Enhancement of...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online