Neural Comput & Applic (****) **:** **

DOI **.***7/s00521-007-0106-x

ORIGINAL ARTICLE

Scanned images resolution improvement using neural networks

Antigoni Panagiotopoulou Vassilis Anastassopoulos

Received: 1 August 2006 / Accepted: 1 February 2007 / Published online: 21 March 2007

Springer-Verlag London Limited 2007

Abstract A novel method of improving the spatial sequences (video) [3, 4]. In general, interpolation tech-

resolution of scanned images, by means of neural networks, niques produce a ne resolution image from a given LR

is presented in this paper. Images of different resolution, image. A great volume of reports on image interpolation

originating from scanner, successively train a neural net- using neural networks (NN) can be found in the literature

work, which learns to improve resolution from 25 to 50 [2, 3, 5 17]. The present work, which attempts to obtain a

pixels-per-inch (ppi), then from 100 to 200 ppi and nally, HR image by successively training the same NN, belongs

from 50 to 100 ppi. Thus, the network is provided with to this category. Neural networks describe systems without

consistent knowledge regarding the point spread function an explicit physical and mathematical modeling. Actually,

(PSF) of the scanner, whilst it gains the generalization the NN is trained to successfully carry out the solution of

ability to reconstruct ner resolution images unfamiliar to inverse problems, which in our case corresponds to

it. The novelty of the proposed image-resolution- restoring effects caused by scanner non-idealities.

enhancement technique lies in the successive training of Super-resolution techniques constitute a quite different

the neural structure with images of increasing resolution. image-processing tool. It is actually the estimation of a

Comparisons with the image scanned at 400 ppi dem- single HR image from multiple LR ones with subpixel

onstrate the superiority of our method to conventional shifts. The resulting image is much closer to the desired

interpolation techniques. one, compared to that obtained using interpolation tech-

niques [4]. Actually, frequency unfolding is achieved,

Resolution improvement Neural network which resolves details in the image ner than the sensor

Keywords

Scanner resolution limit. Neural networks have been used for

solving the super-resolution image reconstruction problem

[5, 6]. Nevertheless, it should be mentioned that from time

to time interpolation techniques are referred to as super-

1 Introduction

resolution, resulting in confusing naming.

Interpolation methods by means of neural networks have

Low-resolution (LR) images are commonly found in many

been employed for resolution enhancement in the area of

imaging applications, such as remote sensing, surveillance

remotely sensed images. An approach of spatial resolution

and astronomy. Over the last two decades, research has

improvement of a remotely sensed, LR, thermal infrared

been devoted to the problem of reconstructing a high-res-

image is described in [7]. The resolution improvement is

olution (HR) image from a LR frame. Basically, interpo-

carried out by means of two three-layered, backpropagated

lation techniques have been used to improve resolution in

NN architectures. Another approach of resolution

medical images [1], digital photos [2], as well as in image

improvement in remotely sensed images is that of a fully-

interconnected NN model [8]. The network used during

A. Panagiotopoulou V. Anastassopoulos testing is composed of three layers. Image interpolation has

Electronics Laboratory, Physics Department,

also been carried out by means of radial basis function

University of Patras, Rio 26500, Greece

neural networks in [9]. The hidden layer neurons of the

e-mail: abpihl@r.postjobfree.com

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40 Neural Comput & Applic (2008) 17:39 47

image in order to construct the 400 ppi HR image. The

network which is employed to perform interpolation

proposed method outperforms the bicubic and spline

compute Gaussian basis functions. The receptive eld

algorithms, which belong to classical interpolation tech-

widths (standard deviations) of these functions are deter-

niques. The experimental results as well as the visual

mined by an adaptive scheme. Furthermore, HR image

comparisons prove the predominance of the proposed

reconstruction has been successfully carried out using

method. Indeed, the inversion of the scanner PSF and

multilayer perceptron networks in [10]. It should be noted

generalized image interpolation can be considered equiv-

that two different backpropagation algorithms are used,

alent. When performing interpolation at 200 ppi to obtain

whereas a multilayer perceptron network is also employed

the higher resolution image of 400 ppi, an effort towards

in [11]. The speci c single-hidden-layer neural network,

getting back missing samples of the image is made. Like-

being trained by the backpropagation algorithm, is required

wise, the NN, having as input the 200 ppi resolution image,

to enhance the resolution of diffraction-limited, binary

results in the 400 ppi image estimating the image samples

images. Moreover, a high-resolution, multi-neural network,

which were discarded during this image acquisition. Con-

based on the local variance is proposed in [12]. This spe-

sistent pieces of information regarding these samples can

ci c network is composed of two neural networks, namely

be obtained from the procedure applied to the HR image by

the NN for low local variance and the NN for high local

the scanner. The novelty of our approach lies in the pre-

variance. The weighted sum of the two NN outputs rep-

sentation order of image-pairs with different resolution

resents the enlarged image. A novel image interpolation

used to train the neural network. To the authors knowledge

scheme, using an arti cial neural network, is described in

there has not been any similar work in literature so far,

[13]. A single frame interpolation algorithm is joined to-

examining the importance of resolution order when pre-

gether with an adaptive, linear, single-layer neural network

senting a neural network with training data.

that models the residual errors between the interpolated

In Sect. 2 of this paper, the performance of the scanner

image and the respective original one. A novel image

as image source is presented. Section 3 consists of a de-

interpolation algorithm by means of a feedforward neural

tailed description of the training procedure, while Sect. 4

network, based upon classi cation, is thoroughly consid-

deals with the results of the conducted experiment. Sec-

ered in [14]. A HVS-oriented, adaptive interpolation

tion 5 presents some decisive aspects concerning our

scheme for natural images by means of neural networks is

treatment of the neural structure for the prementioned

proposed in [15]. Furthermore, a color interpolation tech-

purpose, whereas conclusions are drawn in Sect. 6.

nique for a single-chip CCD camera, employing neural

networks, is presented in [2]. A Hop eld-network-based

algorithm, serving for the resolution enhancement of dis-

crete targets taking up more space than the sample spacing 2 Using scanner as image source

of an image, is dealt with in [16]. Multilayer neural net-

works have also been used to perform document resizing in Scanning devices are able to produce images of the same

[17]. scene characterized by different resolution. The whole

In this paper a technique that performs image interpo- procedure that the scanner applies to an image is described

lation using neural networks is presented. A multilayer by

feedforward (MLFF) neural network, trained by the back-

n hx y io

propagation algorithm, is employed. The scanner provides xy

is n; m o x; y g x; y rect ; comb ;

all the input and output data that the network training re- ab xs ys

quires. Pairs of varying resolution images, obtained

1

through scanner, are used to successively train the network,

so that it becomes familiar with the scanner point spread where is (n, m) is the ensemble of the produced detector

function (PSF). More speci cally, the neural network is signals, o(x, y) is the object scene (input signal), g(x, y is

x y )

trained with the purpose of becoming able to reconstruct a the point spread function of the front optics and rect a ; b is

ner resolution image, even when its input is presented

the detector pupil. The 2D comb-function comb xxs ; yys

with images of different scenes from those than that it has

describes the scanning process. The symbol * denotes 2D

been trained for. Indeed, the resulting network improves

convolution. Scanner PSF is given by relation (1) if o(x, y)

the resolution of scanned images. Scanner, while recording

denotes a delta function. In accordance with the typical

an image, applies a kind of lowpass ltering to this original

de nition of scan directions, spatial scanning in the

high quality image and afterwards, by means of a speci c

direction of the detector line is called in-scan direction,

sampling, obtains the desired lower resolution image. In

while the orthogonal direction, which is along the direc-

this work the NN applies the inverse procedure of the

tion of scan motion for a 1D array, is given the name

scanner PSF, to a 200 pixels-per-inch (ppi) resolution

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Neural Comput & Applic (2008) 17:39 47 41

cross-scan. Altogether it represents a 2D scanning process, followed, images of six different scenes are obtained from

which can be assumed to be separable and described in the scanner and subsequently used in the training and

both directions as a 1D process. During scanning with a simulation procedures. Changing the above mentioned

single 1D detector array, the spatial scanning frequency ks sampling distance p, each scene is recorded at 25, 50, 100,

is in reverse proportion to the middle distance p of 200 and 400 ppi resolutions.

neighboring detector pixels. This spatial sampling fre-

quency 1/p is equal to the distance of the delta impulses in

the comb-function [18]. During image acquisition, differ- 3 Training procedure

ent values can be assigned to the characteristic parameter p,

resulting in images of different resolution. Thus, the comb- A MLFF neural network is employed to perform the image

function is the element of (1), which takes various values resolution enhancement task. The speci c neural architec-

during the recording of different resolution images. ture is shown in Fig. 2. It is a hierarchical network con-

Moving from a high-resolution image to a lower-reso- sisting of an input layer, a hidden layer and an output layer.

lution one, a group of pixels is replaced by one pixel. In Each neuron of the input layer is connected through syn-

theory, this replacement is determined by the scanner optic weights to all the hidden layer neurons. The neurons

characteristics [18, 19]. A large pixel of LR image can be of the hidden layer are also connected to the four neurons

obtained from a group of HR image pixels, as shown in of the output layer in a similar way. Three layers are the-

Fig. 1, taking into consideration the scanner PSF and the 2D oretically suf cient to cope with any application. Usually,

sampling theorem. In case that the PSF of the scanner is the hidden layer possesses the greater number of neurons,

known, the unique evaluation of the LR image pixel from its as the contribution of these neurons to the successful

HR image counterparts can take place. Nevertheless, the execution of the desired mapping is considered crucial.

reverse procedure is not possible by means of a single LR Nevertheless, in the present case, experimentation proved

image, even if the scanner PSF is known. In our case, the

scanner PSF is considered unknown, thus the proposed

approach is universal, whereas the resulting neural network

succeeds in performing the inverse procedure of the scanner

PSF. This leads the network to enhancing the resolution of a

given image not having previously been trained upon it.

The reason for creating a neural network model with the

aforementioned ability lies in the scanner s capability of

providing us with varying resolution images. The use

of scanned images in this work provides the advantage of

having both the input and output vectors required to train

the network from the original source. Otherwise, LR images

have to be created from the HR data by means of decimation

procedures, an approach that demands knowledge of scan-

ner characteristics [18, 19]. In the experimental procedure

Fig. 2 The neural network architecture used at the proposed

interpolation approach. It is a fully interconnected structure, with

Fig. 1 The arrangement of pixels used to train the neural network. all outputs from the rst layer (left) going to all neurons of the second

The white pixels come from the low-resolution image whereas the (hidden) layer. In the same way, all outputs from the second (hidden)

black-shaded ones originate from the one-step-higher resolution layer are fed to all four neurons of the output (third) layer. Each

image. At the simulation procedure the aforementioned four pixels are neuron of the input layer is fed with the values of the 25 white pixels

created at the network output shown in Fig. 1

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42 Neural Comput & Applic (2008) 17:39 47

that the rst two layers should have equal number of images, the neural network is supplied with consistent

neurons. These two layers consist of ten neurons, while the pieces of knowledge as far as the scanner PSF is concerned.

third one of four neurons, as required by the dimensions of In this way, it is successfully taught how to construct an

the output vector. The neural network performance has image characterized by resolution not previously presented

been also tested using, at the rst and second layers, the to it. The obtained resolution is higher than that of each of

following numbers of neurons: 5 and 10, 7 and 10, 10 and the images used as training data. Indeed, the resulting

15, 10 and 20 respectively, but the results were poorer neural network, when presented with a 200 ppi resolution

compared with those obtained using ten neurons. The image illustrating whatsoever scene, produces a satisfac-

backpropagation learning method is applied to the network. tory image of 400 ppi resolution.

Like the delta rule, it is an optimization procedure based on The neural network is not reset from one training stage

gradient descent that adjusts weights to reduce the system to another. Thanks to it, knowledge transfer regarding

error or cost function. The proposed training procedure of resolution improvement takes place from stage to stage.

the neural network is shown in Fig. 3. The training data The resolution order of the training data presentation to the

consist of four different resolution images, which illustrate network is another decisive point. The suggested presen-

the scene poros shown in Fig. 4. Speci cally, the ima- tation of the images results in an ef cient learning proce-

ges poros of 25, 50, 100 and 200 ppi resolutions are dure. Furthermore, the MSE obtained during training is of

order 10 2. Nevertheless, it is of crucial importance that the

employed to train the neural structure. These data are

successively fed to the network input and output. There is a network acquires satisfactory generalization ability. The

serial implementation of three training stages. It should be generalization ability is related to the successful recon-

stressed that each training stage deals with increasing res- struction of a 400 ppi resolution image, when the speci c

olution images of the scene poros . network is simulated with a 200 ppi resolution image. The

At the rst training step, the network is fed with the reconstruction should be successful not only in terms of the

25 ppi resolution image and the output consists of the image poros, as being the network input, but of various

50 ppi resolution image. Afterwards, the images with 100 scenes images as well. As it is shown below, the neural

and 200 ppi resolution are used to form the network input network obtains the desired generalization ability.

and output, respectively. At the nal, third step, the neural

network goes through training with the 50 ppi resolution

image as input and the 100 ppi resolution image as output. 4 Experimental results

At every one training step, the input image is given per

25-component vectors, while the output image is presented After the training procedure has been completed, the ob-

per 4-component vectors. These 25-element vectors are tained neural network goes through the simulation proce-

progressively selected from the LR image in such a way dure. The image poros is used for training the neural

that the whole image is covered. As shown in Fig. 1, at the network. During simulation, ve unfamiliar to the network

HR image reconstruction, the central pixel of each of these images, illustrating various scenes of resolution 200 ppi,

sets is replaced by four new pixels coming from the one- are successively presented to its input along with the image

step-higher resolution image. Each of the 4-element vectors poros of the same resolution. One of these new images,

that corresponds to the neural network output, is used to the image pylos, is shown in Fig. 5. Images of 400 ppi

form the high-resolution image. Thus progressively, all the resolution, which prove satisfactory when compared with

pixels of the low-resolution image are substituted and this the corresponding scanner-originated ones, are created at

image is transformed to the one-step-higher resolution one. the neural network output. Additionally, the trained neural

Due to the fact that every pair of images employed during structure is tested with noisy data and the results are in

each training stage is made up of increasing resolution favor of the proposed method.

Fig. 3 The proposed method of

creating a HR image is based on

the successive training of the

same neural network with

different resolution images

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Neural Comput & Applic (2008) 17:39 47 43

Fig. 4 a The image poros of

400 ppi resolution coming from

the neural network. b The image

poros of 400 ppi resolution

obtained using the bicubic

algorithm. c The image

poros of 400 ppi resolution

obtained using the spline

algorithm. d The image

poros of 400 ppi resolution

coming from the scanner

4.1 Numerical and visual comparisons image obtained by the proposed method, approach the

original values more ef ciently compared with those of the

Besides visual comparison, our method is also assessed by images obtained by means of the two classical interpolation

means of mean square error (MSE), mean and standard methods. Visual comparison, Figs. 4 and 5, also proves the

deviation values in comparison with the bicubic and spline predominance of the proposed method. Comparison results

interpolation algorithms. Tables 1 and 3 report on these regarding the rest four images used during simulation have

estimated values as far as the image poros is concerned. shown to conclude in favor of the proposed method, too.

The proposed serial training procedure leads the neural From the above analysis it is obvious that the image

network to a 400 ppi resolution image that demonstrates resolution improvement carried out by means of our neural

MSE = 0.0405. This value is lower than the MSE = 0.0433 network outperforms the corresponding results of the spline

evaluated for the images resulting from the bicubic and and bicubic interpolation algorithms. It should be stressed

spline algorithms. Moreover, the mean value of the net- that the proposed method leads to a neural network, which

work-produced image is equal to 0.5263 and approximates is not specialized in improving the resolution of a speci c

satisfactorily the original image one, which is equal to scene image. The employed neural structure, through the

0.5340. Images coming from the bicubic and spline presented training procedure, learns how to improve image

methods display mean values 0.5200 and 0.5034, respec- resolution and thus successfully constructs HR images not

tively, which are quite distant from the mean value of the having previously been trained upon. All the MSEs are

scanner-originated image. Furthermore, the standard devi- evaluated using the 400 ppi resolution image coming from

ation value, equal to 0.2329, of the network-produced the scanner. It is noted that the range of the pixel values is

image is quite close to the desired value of 0.2615 in 0 1, whereas the programming tool used is MATLAB.

contradiction with the values 0.2103 and 0.1921 of the

bicubic and spline algorithms images. 4.2 Edge details assessment

Additionally, the obtained neural network demonstrates

satisfactory results when it is simulated with images that In order to provide a more detailed assessment of the

have not been used during its training procedure. Tables 2 proposed method a small part of the image poros,

and 3 contain numerical results for the image pylos . The shown in Fig. 6, is acquired by means of the scanner at 25,

0.0409 MSE of our method is quite lower than the 0.0428 50, 100, 200 and 400 ppi resolutions. This speci c scene

and 0.0424 errors of the bicubic and spline interpolations. image is then used to train the neural network of Fig. 2. Its

Furthermore, the mean and standard deviation values of the simulation with the 200 ppi resolution image leads to the

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44 Neural Comput & Applic (2008) 17:39 47

Fig. 5 a The image pylos of

400 ppi resolution coming from

the neural network. b The image

pylos of 400 ppi resolution

obtained using the bicubic

algorithm. c The image

pylos of 400 ppi resolution

obtained using the spline

algorithm. d The image

pylos of 400 ppi resolution

coming from the scanner

Table 1 Results in terms of mean and standard deviation values for Table 2 Results in terms of mean and standard deviation values for

the image poros of 400 ppi resolution the image pylos of 400 ppi resolution

Image origin Mean value Standard deviation value Image origin Mean value Standard deviation value

Network output 0.5263 0.2329 Network output 0.4773 0.1743

Bicubic 0.5200 0.2103 Bicubic 0.4976 0.1684

Spline 0.5034 0.1921 Spline 0.5066 0.1594

Scanner 0.5340 0.2615 Scanner 0.4844 0.2207

image of 400 ppi resolution illustrated in Fig. 6a. The avoiding the introduction of even small artifacts in com-

corresponding result obtained by means of the bicubic parison with the bicubic algorithm. The above result is

method is shown in Fig. 6b. Visual comparison proves the quanti ed via the edge correspondence measure. Prewitt

predominance of the proposed method as far as edge pre- edge detector is used to nd edges in the 400 ppi image of

serving is concerned. Our neural network achieves detailed Fig. 6a, in the corresponding image coming from the bic-

and exact reconstruction, while the conventional interpo- ubic method and in the original small part of the image

lation technique is not satisfactorily edge preserving. The poros originating from the scanner. Then, the number of

outline of the small window cited just above the door next correct edges that the images of Fig. 6a and b present is

to the white car and the right side of the black door coming calculated. The image of the proposed method has 68

immediately after the dark coloured car indicate that the correct edges, while the image obtained by means of the

proposed method is more capable of retaining edges and bicubic algorithm presents 44 correct edges. The spline

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Neural Comput & Applic (2008) 17:39 47 45

transfer function. The Powell-Beale version of the conju-

Table 3 Results in terms of MSE for the images poros and

pylos of 400 ppi resolution gate gradient algorithm is the selected training function.

Furthermore, the training parameter concerning the mag-

Real output origin MSE between real output and target output

nitude of the gradient is set to the value 10 5, so that it can

Poros Pylos

be never reached. As far as the learning parameter is

concerned, it is set equal to 10 2. The number of training

Network output 0.0405 0.0409

epochs for each stage is 250. Each output image recon-

Bicubic 0.0433 0.0428

struction requires the estimation of (n1 4) (n2 4)

Spline 0.0433 0.0424

4-component vectors, where n1, n2 denote the number of

the input image pixels in the vertical and horizontal

directions, respectively. The calculation of each of the

above 4-component vectors is performed by a neural

network that functions having 25 10 + 10 10 +

10 4 = 390 weights. The number of coef cients used by

the bicubic interpolation algorithm is 13, while the spline

interpolation uses 361 coef cients. Thus, the computa-

tional complexity of our neural-network-based method is

of the same order as that of the spline interpolation, but of

higher order than that of the bicubic interpolation.

4.5 Remarks

Fig. 6 a A small part of the image poros of 400 ppi resolution

coming from the neural network. b A small part of the image poros

It should be mentioned that during the training procedure

of 400 ppi resolution obtained using the bicubic algorithm. It is

the neural network may reach a speci c value of training

obvious that the proposed method outperforms the bicubic interpo-

MSE but cannot proceed to a lower level. If it is forced

lation as far as edge preserving is concerned

by the number of training epochs to repeatedly reach this

MSE value, due to continuous training, it becomes un-

algorithm outcome is not presented here but is the same as able to ful ll our expectations concerning generalization

that of the bicubic algorithm. ability. More clearly, overtraining occurs because the

neurons memorize the training set patterns and become

4.3 Performance under noisy conditions unable to recognize target-class patterns that were not

included in the training set. Thus, using a number of

Furthermore, the performance of the obtained neural net- epochs larger than the 250 selected is not recommended.

work under noisy conditions is examined. Gaussian noise In contrast, fewer than 250 training epochs are inade-

with variance 25 is added to the image poros of 200 ppi quate for ef cient learning of the neural network.

resolution, which is then fed to the network input. This Overtraining also takes place in case of using more than

degraded image is shown in Fig. 7a. The 400 ppi resolution ten neurons in the rst two layers, whereas fewer than

image produced at the network output is depicted in ten neurons are insuf cient in view of our purpose.

Fig. 7b. Figure 7c and d illustrate the result of creating a Additionally, when choosing a value of the learning rate

parameter higher than 10 2, the whole procedure may not

400 ppi resolution image from the degraded 200 ppi res-

olution image, by means of the bicubic and spline inter- converge to the appropriate network weight values [20].

polation algorithms. It is visually obvious that the proposed Numerous combinations of activation functions used by

method outperforms the aforementioned two conventional the neural network neurons have also been tested. This

interpolation techniques. The neural network results in an aforementioned combination tansig tansig logsig was the

image with noise almost eliminated, in contradiction with most ef cient. Moreover, the presentation order of

the images coming from the bicubic and spline interpola- training data to the neural network has proved to be of

tion algorithms. great signi cance, as well. The serial training procedure

has also been tested, with input output image pairs

4.4 Computational details 25 50, 50 100 and 100 200 ppi. However, the obtained

network appears to possess limited abilities. Furthermore,

The neurons of the rst two layers use the tan-sigmoid various combinations of the prementioned three training

transfer function to generate their outputs, while the acti- stages for the implementation of a nine-stages-repetitive

vation function of the last layer neurons is the log-sigmoid training procedure have been examined. Nevertheless,

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46 Neural Comput & Applic (2008) 17:39 47

Fig. 7 a The image poros of

200 ppi resolution degraded by

Gaussian noise. b The image

poros of 400 ppi resolution

coming from the neural network

when it is presented with the

degraded 200 ppi resolution

image. c The image poros of

400 ppi resolution obtained

when performing bicubic

interpolation at the degraded

200 ppi resolution image. d The

image poros of 400 ppi

resolution obtained when

performing spline interpolation

at the degraded 200 ppi

resolution image

this repetitive implementation did not prove ef cient as that neural structures cannot easily retain the knowledge

the resulting image was over-smoothed. gained by means of a speci c set of training data when a

new training data set is presented to them. Furthermore,

during the training procedure the network employed for

interpolation is provided with consistent knowledge of the

5 Discussion

standard deviation and mean values of the image of ner

resolution. In reverse, when creating an interpolated image

The fact that the neural network weights are not reinitial-

through the bicubic and spline methods, there are no

ized from training step to training step on the one hand, and

a priori pieces of knowledge of these characteristic values

the order the training images are fed to the network on the

of the constructed image.

other, are two decisive points of the presented strategy.

Both result in retaining the neural network knowledge from

one training stage to another. An additional signi cant

point is the neural structure training with images coming 6 Conclusions

directly from the scanner. Due to the originality of this

training procedure, the network is provided with the In this work, a multilayer feedforward neural network is

appropriate pieces of knowledge, so that it can learn to employed to improve the resolution of scanned images.

improve image resolution. Moreover, when treating a Pairs of different resolution images, illustrating the scene

neural network special attention should be paid to avoid poros and coming from the scanner, are used in the

overtraining. The number of the training epochs as well as training procedure. There are three training stages each of

the number of the neurons in each layer must be carefully which deals with a pair of increasing resolution images. The

selected. It should be stressed that the low value of the neural network gains knowledge concerning resolution

MSE obtained during training does not keep pace with a enhancement from 25 to 50 ppi, from 100 to 200 ppi and

visually satisfactory image of 400 ppi derived from the from 50 to 100 ppi. In this way it achieves to ef ciently

network, as it differs from the adequate generalization learn the PSF of the scanner. The rationale behind the

ability. This ability is indeed connected to the desired, proposed approach is the training of the network in order

successful reconstruction of the HR image. Nonetheless, it not only to learn how to improve a speci c image resolution

should be stated that the visual inspection remains the most to a ner one, but to generalize this knowledge to even ner

reliable measure of comparison. Moreover, it is evident resolution and different scene image, as well. Actually, the

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Neural Comput & Applic (2008) 17:39 47 47

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