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2012 5th International Congress on Image and Signal Processing (CISP 2012)

An Improved Nonuniformity Correction Algorithm for IRFPA

Shaosheng Dai, Yongpeng Liu, Qiang Zhang, Yang Zhang, Minxiang Bian Chongqing Key Laboratory of Signal and Information Processing,

Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

Abstract—To improve nonuniformity correction real-time performance of infrared focal plane arrays (IRFPA), a novel compression nonuniformity correction algorithm is proposed. In this paper the basic principle of the proposed algorithm are expounded, and its real-time performance is tested by hardware. Experimental results show that the proposed algorithm has the less calculation and the higher speed than traditional nonuniformity correction algorithm based on blackbody, and this helps to realization of real-time nonuniformity correction of IRFPA.

Keywords: IRFPA; compression nonuniformity correction; real-time performance

I. INTRODUCTION Because of influence from material, manufacture and

environment, each pixel of IRFPA response exists the nonuniformity, this limits infrared imaging system detecting capability, and it is difficult to meet the demands of practical application. So before using IRFPA it must be corrected[1]. Conventional methods of IRFPA nonuniformity correction deal with a great deal of data, slow correction speed, and it could hardly meet the need of real-time correction. During nonuniformity correction experiments we found that many same response pixels were repeatedly corrected. In order to eliminate the redundance calculations, a novel compression nonuniformity correction is proposed. The new method can greatly improve infrared image correction speed by means of combining the same gray value and decreasing the calculations.

We compare the residual nonuniformity and correction speed of correction algorithm based on blackbody and the proposed algorithm. The result proved that the proposed compression correction has less data of calculation, faster real-time correction speed, and perfect correction precision.

II. THE COMPRESSION CORRECTION PRINCIPLE Conventional method of correction based on blackbody

needs to correct each pixel. Although the algorithm is fixed and simple, it demands to calculate a great deal of data and can hardly real-time processed. Therefore, we proposed the compressing correction method in our article could obtain not only higher correction precision, but also faster speed.

A) Compression correction principle. We all know that the maximal gray value of infrared image is 255, and the gray level

Fig.1 The compression correction principle diagram is 256, no matter how many pixels an image is, the gray values of pixels are in the domain of [0,255]. If an image which pixels are more than 256, then two pixels have the same gray value at least. So we could combine these pixels which have the same gray value during nonuniformity correction, and encode the pixels which have same gray value in same number, which could be used for decreasing correcting calculation. The encoding table is recorded at the most sensitive pixel which reaches the saturation value in an image when blackbody radiation. The encoding table consists of the numbers between 0 to 255, it includes the mapping relationship of pixel number and encoding table. Therefore it will generate several batch calibration compression data which will be used for nonuniformity correction. When system starts the correction, the encoding table is looked up and correction coefficients are obtained. Finally nonuniformity compression correction is calculated by formula (1). Fig.1 is the compression correction principle diagram. Formula (1) is two point’s compression correction formula. kG and kO is gain and offset of two

Corrected output data

Pixel numbers

Calibration data

A/D sampling

Coding numbers

Encoding

12 12 30 ……250 255

1 2 3 …… 128×128

1 2 …… 221

353

points compression correction, ijx is input value of different

pixels before correction , ijy is output value of different pixels after correction.

kijkij OXGY += )(φ (1)

⎪⎪⎩

⎪⎪⎨

⎧

−−

=

−−

=

)()()()(

)()(

HXLXHXVLXV

O

LXHXVV

G

kk

kLkHk

kk

LHk

(2)

Formula (2) is used for calculating gain coefficient and offset coefficient. The HV is the max output voltage that pixels

in high temperature HT . The LV is the max output voltage

that pixels in low temperature LT . The k is pixel code number. The Xk(H) and Xk(L) are separately output voltage of the k pixel in HT and LT temperature.

B) Hardware processing of the proposed compression correction. Infrared imaging system mainly consists of infrared focal plane arrays (IRFPA), high speed A/D switcher, CPLD, DDR2 memory, TMS320DM6437 DSP[3] and LCD. The frame chart of the system is as follows. Fig.2 The chart of infrared imaging system

We develop VC++6.0 program in order to record two sets of calibration data, and the corresponding encoding table is generated. Finally according to equation (2), the correction gain and offset of every pixel are calculated. Pixels with the same code have the same gain and offset.

III. THE COMPARISON BETWEEN PROPOSED CORRECTION AND TWO POINTS CORRECTION

A).The comparison of processing speed. In the real-time correction processing, multiplication and addition operation for each pixel are executed at least once, and comparison operation is carried out at least twice. Therefore, the more pixels of IRFPA, the more calculation will be done and the processing speed of DSP is more slowly.

In experiment IRFPA has 320×240 pixels, which frame frequency is 50 frames per second. Therefore we compare system processing speed between the proposed algorithm and two points correction algorithm. The results are as follows: Table 1 Processing speed comparison from two correction algorithms

The experimental results proved that the processing speed of proposed algorithm is faster than two point’s correction.

B).The comparison of residual nonuniformity after correction. The residual nonuniformity of the proposed correction and two point’s correction is calculated in Table 2. We use the third national standard formula for calculating the nonuniformity of IRFPA. The calculation equation is as follows:

∑∑= =

−+−×

=M

i

N

joavgij

oavg

VVhdNMV

NU1 1

2)()(

11 (3)

∑∑= =+−×

=M

i

N

jijoavg V

hdNMV

1 1)(1

In formula NU is nonuniformity of IRFPA, Vij is output voltage of i row and j line pixel, Voavg is average output value of all pixels in IRFPA, M and N are the numbers of row and line of IRFPA, d is number of dead pixels which in IRFPA, h is number of over hot pixels. Residual nonuniformity is compared as follows. Table2 Remnant nonuniformity comparisons of two corrections nonuniformity image

The same radiation blackbody image

uncorrection image 18.1% two correction image 3.6% proposed algorithm correction image

2.6%

C).The three dimensional curve comparisons between two corrections algorithms. We compare the correction results of two algorithms by using the same infrared image. In fig.3(a) the image without correction has worse uniformity. In fig.3(b) the image with two points correction has good uniformity. In fig.3(c) the image with proposed correction also has good uniformity.

Name The proposed correction

Two points correction

Processing speed (frames/per second)

30 22

384×288 IRFPA

High speed A/D

CPLD

TMS320DM6437 DDR2

LCD

354

050100

150200250

0 50 100 150

pixelsgr

ay

uncorrected image and its one dimensional curve

(a)uncorrected image and its three dimensional curve

0

50

100

150

200

250

0 50 100 150

pi xel s

gray

Nonuniformity correction image and its one dimensional curve of two points correction

(b) Nonuniformity correction image and its three dimensional curve of two points correction

0

50

100

150

200

250

0 50 100 150pi xel s

gray

Nonuniformity correction image and its one dimensional curve of the proposed algorithms

(c) Nonuniformity correction image and its three dimensional curve of the proposed algorithms Fig.3 The three dimensional curve comparisons of two nonuniformity correction algorithms

IV. CONCLUSIONS In this paper, we proposed a novel method that is the

compression correction algorithm. The algorithm can decrease calculation during infrared image processing and improve the speed of correction. The experimental comparison of correction speed, image residual nonuniformity and three dimension curve between two algorithms. The results show that the proposed correction algorithm has not only better correction precision, but also higher correction speed. Therefore the proposed correction methods can be more fitful for real-time processing of infrared image in future.

ACKNOWLEDGEMENTS This work is supported by the NSAF Foundation

(No.10776040) of National Natural Science Foundation of China, the National Natural Science Foundation of China (No.60602057, No.61275099), the Project of Key Laboratory of Signal and Information Processing of Chongqing (No.CSTC2009CA2003), the Natural Science Foundation of Chongqing Science and Technology Commission (No. CSTC2010BB2411, CSTC2010BB2398, CSTC2006BB2373), the Natural Science Foundation of Chongqing Municipal Education Commission (No.KJ060509, KJ080517), and the Science and technique foundation of Chongqing (CSTC2011AB2008).

REFERENCES [1] Yin Shimin, Liu Shangqian. The multi-point nonuniformity correction

algorithms for IRFPA based on low order interpolation. Acta photonica sinica, 2002, 31(6):715-718.

[2] Xing Suxia, Zhang Junjv, Sun Lianjun, eta1. Two-point nonuniformity correction based on LMS．SPIE，2005，5640：l30-136．

[3] Shi Yan, Zhang Tianxu，Li Hui,etal. New approach to nonuniformity correction of IRFPA with nonlinear response. Journal of Infrared and Millimeter Waves,2004,23(4):251-256.

[4] Feng Lin, Liu Shuang, Zhao Kaisheng, Guan Anquan. Method of nonuniformity correction for IRFPA with nonlinear response.Journal of Infrared and Millimeter Waves,2006,25(3): 221-224.