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Introduction

The super resolution (SR) methods used in image reconstruction aim at reconstructing a high resolution

(HR) image from several low resolution images (LR). The goal is to improve the spatial resolution in

images. The HR reconstruction problem is challenging, because it is ill posed problem. SR methods have

application in every field where digital images are used, and where there is an additional need to

improve in the image resolution. For example, we can found SR method in medical imaging, HDTV,

satellite images, face recognition

Because of important advantages: cheaper cost than upgrading the hardware to produce higher

resolution pictures, and can be used to improve resolution further in the case of hardware limit. SR

methods can be used in both spatial and frequency domains. The type of domain used depends on the

problem and both domains have their advantages and disadvantages. We will make detail about this

problem later. SR methods also can take from one or multiple images and depend on the quality criteria

and purpose (real time processing, information) we chose the suitable ways for this.

Beginning from 1984, after two decades, image super resolution have a rapid development and

thousands papers in this topic are published. In my survey, I want to cover relevant approaches

introduced later and in this way map current development of super resolution techniques. We do not

contemplate to go into details of particular algorithms or describe results of comparative experiments,

rather we want to summarize main approaches and point out interesting parts of the super resolution

methods.

Super resolution method:

Single image SR

We will here discuss some simple methods to SR images, when there is only one LR image available. The

first methods can be used to zoom an LR image, but they are not true SR methods in the sense that they

do not use information from several LR images for reconstructing the HR images. However, these

methods still try to achieve the same end result, and are sometimes called quasi-super resolution

methods.

Interpolation is simplest way to do this problem. In general, using nearest neighbor, cubic or bilinear

interpolation are faster but also produce unwanted artifacts (blocking, ringing, edge halos). Bi-spline

interpolation is commonly used to enlarge digital images, while trying to avoid the artifacts. By matching

with a high order function, we can get a smooth on background but sometime is not good at texture or

edge. Another common interpolation is 2D sinc interpolation, but it no better than bi-spline

interpolation. We also do interpolation at frequency domain by insert zero at spectrum of image and

then transfer it back to spatial domain. It is the fastest way to zoom but extra no information. Although

computationally cheap, interpolation methods are not the best way to perform.

Another way to zooming an image is to use a wavelet transform ([3], [4]). These methods are much

better than the discrete Fourier transform to analyze frequencies. Being a wavelet method, the image

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operations are performed in the frequency domain, and then apply invert wavelet transform to the

result. The unknown wavelet coefficients are computed in HR grid by averaging on two neighbor

coefficient from the LR image. .

Only using one image but plus information about systems, [learning low level resolution, W.T Freeman

and SR through neighbor embedding, Hong chang] perform SR by training system and [] do it by usingstatically methods.

In training algorithms, we use a training set containing sharp nature images with low, mid and high

frequency. The Inputimage is the image you want to zoom in. First you have to scale it up interpolating

the missing pixels. You then obtain a bigger image, but the high frequency data is missing. SR can be

seen as two main independent steps : the first one consists in preparing the Training Set in a way that

will permit the second one to construct the high frequency band that is missing in the scaled up input

image. In first step, training set generation is performed by Markov network algorithm or pre-processing

function for single pass algorithm. After that,

In [], training system perform by using Markov network and some kind of natural images. For given input

image y(after been pre-processed), we seek to estimate the underlying scenex. The image yis made of

observation nodes (the low-resolution patches), which have an underlying explanation, the high-

resolution patches. In figure 1, the lines indicate statistical dependencies between the nodes. The

Training Set is used to compute the probability matrice Y (representing the horizontal relation between

high-resolution nodes), and F (representing the vertical relation between high-resolution and low-

resolution nodes).