Super-resolutionimagereconstruction-atechnicaloverview - CSDN文库

Super-Resolution,image

registration,image

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SUPER-RESOLUTION

Pradeep Gaidhani

1. INTRODUCTION

The central aim of Super-Resolution (SR) is to generate a higher resolution image from

lower resolution images. High resolution image offers a high pixel density and thereby more

details about the original scene. The need for high resolution is common in computer vision

applications for better performance in pattern recognition and analysis of images. High

resolution is of importance in medical imaging for diagnosis. Many applications require

zooming of a specific area of interest in the image wherein high resolution becomes

essential, e.g. surveillance, forensic and satellite imaging applications.

However, high resolution images are not always available. This is since the setup for high

resolution imaging proves expensive and also it may not always be feasible due to the

inherent limitations of the sensor, optics manufacturing technology. These problems can be

overcome through the use of image processing algorithms, which are relatively inexpensive,

giving rise to concept of super-resolution. It provides an advantage as it may cost less and

the existing low resolution imaging systems can still be utilized.

2. SUPER-RESOLUTION

Super-resolution is based on the idea that a combination of low resolution (noisy) sequence

of images of a scene can be used to generate a high resolution image or image sequence.

Thus it attempts to reconstruct the original scene image with high resolution given a set of

observed images at lower resolution.

The general approach considers the low resolution images as resulting from resampling of a

high resolution image. The goal is then to recover the high resolution image which when

resampled based on the input images and the imaging model, will produce the low

resolution observed images. Thus the accuracy of imaging model is vital for super-resolution

and an incorrect modeling, say of motion, can actually degrade the image further.

The observed images could be taken from one or multiple cameras or could be frames of a

video sequence. These images need to be mapped to a common reference frame. This

process is registration. The super-resolution procedure can then be applied to a region of

interest in the aligned composite image. The key to successful super-resolution consists of

accurate alignment i.e. registration and formulation of an appropriate forward image model.

The figure 1 below shows the stages in super-resolution process.

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评论1

nsnly

2014-06-10

不错，作为super resolution的一个参考很好

anniezhao559

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