International Journal of Video&Image Processing and Network Security IJVIPNS-IJENS Vol:15 No:06 8

154206-1818-IJVIPNS-IJENS © December 2015 IJENS I J E N S

Quality Enhancement of Image with Background

Interference using Statistical Signal Processing Zia-ul-Haq

Department of Electrical Engineering

University of Engineering and Technology

Peshawar, Pakistan

ziaulhaq81@gmail.com

Hafeezul Haq Department of Electrical & Electronic Engineering

Karadeniz Technical University

Trabzon, Turkey

hafeezulhaq88@yahoo.com

Abstract— Interference and noise has been a major area of

concern in any communication system. It is unwanted signal

which affect the transmitted signal during transmission, signal

may be voice, image or video depending upon the system

requirement. However for any type, the noise affects are always

present. In some system interference or noise reduction has more

importance like biometric system. Most of the biometric systems

use images, where the quality is a prime factor. Hence a proper

technique is required to improve the image quality by reduction

of interference. Here we presented an algorithm for improving

the quality of fingerprint images of biometric system. The

technique adopted here for interference or noise reduction and

improving the quality of image is on mean power calculation

based algorithm, in which the mean power will be calculated for

each frame, and certain frame having mean power above the

threshold is accepted and enhanced by a specific value. Finally

the results are compared for structure similarity using mean

structural similarity index.

Index Term— Mean Power Calculation (MPC);

fingerprint; framing; Fingerprint Verification Competition 2004

(FVC2004); Structural SIMilarity (SSIM); Tagged Image File

Format (TIFF); minutiae

I. INTRODUCTION In any communication channel, interference plays a major

role. In case of image transmission, image is corrupted, become noisy and spoiled by losing its quality. In case of biometric system image (fingerprint, palm print, face etc.) information transmission, the quality is very important. Therefore to maintain the quality of transmitted image we require an efficient technique which improves the image quality by reducing the noise during transmission. As all the finger print images is consists of different minutiae [1] i.e. bifurcation, ridge ending etc. And the correct minutiae retrieval is very necessary for a biometric system of identification and matching of fingerprints. Our idea is to develop some useful technique to minimize or remove the noise produced in image (fingerprint) during transmission.

We selected the fingerprint images from the Third International Fingerprint Verification Competition FVC2004, set B of Database 1 (DB1_B) [2] for the testing of our

technique. We tested total of 80 different images in TIFF format with having different minutiae.

Here we showed a noise reduction technique using mean power calculation (MPC) for image transmission. Frame wise average power is calculated and the frame having average power more than a threshold value are accepted and enhanced by a specific calculated value. Power for each frame is calculated [3] by

P = ∑ s (n)2

Where n = -N to N, s is the signal and P is the power of

signal where N is the frame length. Using this concept a set of algorithms is developed for removing or reducing the noise from image and improving the quality. The complete block diagram is shown in Fig 1.

In contrast the algorithm is also tested for taking the image as single frame, and both the processed images obtained by framing and without framing are compared with original image using structural similarity (SSIM) index [4].

A. Collection of fingerprint images and adding noise

All the 80 images in FVC2004 (DB1_B) are contained on pixels with data type uint8 and TIFF format. Therefore before processing these are converted to data type double and then from two dimensional (2-D) to 1-D. Gaussian noise is added using imnoise for different variance values [5] ranging from 1dB to 20dB signal to noise ratio (SNR), and each image is processed for every value of SNR.

B. Framing

To quantize the image, signal framing concept is used. For simplicity and convenience we selected the frame size as 480 pixels per frame. The frame size value is such selected so that we can achieve the best results.

C. Mean Power Calculation

To reduce the noise and improve the image quality mean power is calculated for each frame, the frames whose power is more than a certain threshold value is accepted for further processing.

International Journal of Video&Image Processing and Network Security IJVIPNS-IJENS Vol:15 No:06 9

154206-1818-IJVIPNS-IJENS © December 2015 IJENS I J E N S

The threshold level is implicitly selected to avoid the image formation like a partially rolled fingerprint image during process [6]. Also the threshold value is determined according to the input image under process. This change for each image under process and thus the algorithm nature become adaptive.

D. Image extraction, noise reduction and reconstruction

For effectively noise reduction and improving the image quality, the pixels values in each frame are enhanced by X

The enchantment level is implicitly selected and the value X is chosen such that to get the image in good quality with reduced noise. Finally the image is reconstructed by changing the signal from data type double to data type uint8 and then from 1-D to 2-D. Fig 2 show the results for image 101_2 from database DB1_B, for 5dB and 10dB SNR values. Here it is obvious that the minutiae of the processed images obtained by framing are clearer than the images obtained without framing.

II. RESULTS ASSESSMENTS All the images are composed of pixels and these pixels

show dependency [4]. The structural similarity SSIM index is used for quality assessment of the processed images. To get a single overall quality measure, Mean SSIM (MSSIM) Index is calculated [7].

To analyze the results we first compared the noisy image with original image and then both the processed images obtained by framing and without framing are compared with original image. The MSSIM values for image 101_2 are given in Table I and Table II; these values are plotted in Fig 3 where the x-axis show the SNR ranging from 1dB to 20dB and y-axis show the MSSIM index. It shows that the algorithm is well work in the range of 5dB to 15dB SNR. The improvement is lower for SNR below 5dB due to the higher amount of noise. It also shows that there is more improvement in quality of the processed image obtained by framing in contrast to the processed image obtained without framing.

Fig. 1. Full block diagram of the image noise reduction and

quality enhancement technique

(a)

Collection of finger

print images

Framing of the signal

Mean Power

Calculation

Image extraction and

noise reduction

2-D to 1-D

Adding Gaussian noise

Image

reconstruction

International Journal of Video&Image Processing and Network Security IJVIPNS-IJENS Vol:15 No:06 10

154206-1818-IJVIPNS-IJENS © December 2015 IJENS I J E N S

(b)

(c)

(d)

(e)

(f)

(g)

Fig. 2. (a) Original or input image (b) Noisy image with 5dB SNR (c) Noisy image with 10dB SNR (d) Processed image by framing (for noisy

image of 5dB SNR) (e) Processed image by framing (for noisy image of 10dB SNR) (f) Processed image without framing (for noisy image of

5dB SNR) (g) Processed image without framing (for noisy image of 10dB SNR)

TABLE I.

MEAN STRUCTURAL SIMILIRITY, MSSIM INDEX FOR 1dB to 10dB SNR

Image

number Description

SNR

1dB 2dB 3dB 4dB 5dB 6dB 7dB 8dB 9dB 10dB

101_2

Noisy image 0.1297 0.1629 0.2046 0.2536 0.3115 0.3769 0.4551 0.5389 0.6242 0.7086

Processed image

obtained by framing 0.1255 0.1607 0.2084 0.2756 0.3727 0.4975 0.6506 0.7954 0.8963 0.9502

Processed image

obtained without framing 0.1175 0.1438 0.1861 0.2458 0.3453 0.4823 0.6390 0.7758 0.8766 0.9292

International Journal of Video&Image Processing and Network Security IJVIPNS-IJENS Vol:15 No:06 11

154206-1818-IJVIPNS-IJENS © December 2015 IJENS I J E N S

TABLE II.

MEAN STRUCTURAL SIMILIRITY, MSSIM INDEX FOR 11dB to 20dB SNR

Image

number Description

SNR

11dB 12dB 13dB 14dB 15dB 16dB 17dB 18dB 19dB 20dB

101_2

Noisy image 0.7838 0.8454 0.8944 0.9303 0.9535 0.9701 0.9806 0.9876 0.9920 0.9950

Processed image

obtained by framing 0.9732 0.9808 0.9835 0.9851 0.9862 0.9870 0.9875 0.9877 0.9880 0.9882

Processed image

obtained without framing 0.9494 0.9561 0.9579 0.9587 0.9589 0.9595 0.9596 0.9599 0.9601 0.9602

Fig. 3. Plot of the MSSIM Index for noisy and processed

image (by framing and without framing)

III. APPLICATION

In any communication system a noise reduction technique is always required. This need get more importance in biometric system, especially when there are images (fingerprint, palm print, face etc.) involved, as to properly detect, recognize and match the images. The technique used here sufficiently reduced the noise, improved the quality and clarified the minutiae.

IV. CONCLUSION

Our main aim is to come out with a new technique for noise reduction and quality improvement of image, in the form of fingerprint. MATLAB is used for simulation [8]. The results are obtained for complete database (DB1_B) of FVC2004 for 80 images with satisfactory results.

The entire tests are performed in the form of simulation using MATLAB. The mean power calculation (MPC) based algorithm is used here for manipulating the images in fingerprint format can however be used in future to test the other gray scale images like palm print & face as well as video with some modification. The algorithm tested both with framing and without framing of noisy image with finding the more satisfactory results for framing, after comparison with original image under process.

We thought to compare the results with other such interference and noise reduction techniques in future. And

try to implement our proposed algorithm in hardware circuitry with comparison the results of MATLAB simulation.

ACKNOWLEDGMENT

The author is grateful for their parents, lecturers and friends for support. Especially of Dr. Amjad Ali from Sarhad University of Science and Information Technology Peshawar for support in biometric technology.

REFERENCES [1] S. Maddala and S. R. Tangellapally, "Implementation and Evaluation

of NIST Biometric Image Software for Fingerprint Recognition," Master of Science, Dept. Elect. Eng., Blekinge Inst. of Technology,

Sweden, 2010.

[2] (2000, Sep 14). FVC2004 Fingerprint Verification Competition [Online]. Available: http://bias.csr.unibo.it/fvc2004/

[3] A. Pramanik and R. Raha, "De-noising/noise cancellation mechanism

for sampled speech/voice signal," in Wireless and Optical Communications Networks (WOCN), 2012 Ninth International

Conference, Indore, 2012, pp. 1-4.

[4] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity,"

Image Processing, IEEE Transactions on, vol. 13, pp. 600-612, Apr.

2004. [5] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, “Image Restoration

and Reconstruction,” in Digital Image Processing Using MATLAB, 2

ed. New Delhi: Tata McGraw Hill Education Private Limited, ch.4, sec.4.2, pp. 165-183

[6] Taking Fingerprints [Online]. Available:

http://www.tpub.com/maa/179.htm [7] Z. Wang. (2003, Feb). The SSIM Index for Image Quality Assessment

[Online]. Available:

https://ece.uwaterloo.ca/~z70wang/research/ssim/ [8] W. J. Palam III and W. Palm, Introduction to MATLAB 7 for

Engineers, 2 ed.: McGraw-Hill.

0 2 4 6 8 10 12 14 16 18 200.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Signal to Noise Ratio,SNR (dB)

Mean

Str

uctr

ura

l S

IMil

ari

ty,M

SS

IM i

nd

ex

Noisy image

Processed image obtained by framing

Processed image obtained without framing