Enhancement of the Underwater Images Using Modified Retinex Algorithm

Transcription

1 Al- Mustansryah J. Sc. Vol. 24, No 5, 2013 Enhancement of the Underwater Images Usng Modfed Retnex Algorthm Nabeel Mubarak Mrza 1, Al Abd Dawood Al Zuky 2 and Hazm Gat' Dway 3 1 Department of Physcs, College of Educaton, AL-Mustansryah Unversty, Baghdad, Iraq. 2,3 Department of Physcs, College of Scence, AL-Mustansryah Unversty, Baghdad, Iraq. Receved 16/3/2013 Accepted 15/9/2013 الخالصة صور األجسام تحت الماء غير واضحة للعيان بسبب التباين المنخفض نتيجة التشتت الكبير للضوء ووجود ضوضاء في البيئة المائية مما يؤدي إلى فقدان تفاصيل الصورة كذلك لونها األصلي. خوارزمية (Retnex) تعمل على تحسين مستوى السطوع, التباين والشدة من الصورة. في هذه الدراسة, أقترحنا خوارزمية جديدة لتحسين الصورة تعتمد على تعديل متعدد النطاق مع أستعادة اللون.)MMSRCR( هذه التقنية تستخدم عنصر اإلضاءة في الفضاء اللوني (YIQ) حيث تستخدم Functon) (Sgmod كتحويل لها. العالقة بين المعدل ومعدل األنحراف المعياري تم حسابها لمعرفة كفاءة هذه الطريقة حيث قورنت مع خوارزمية AINDANE وكذلك مع خوارزمية تسوية الهستوغرام االعتيادية,(HE) جميع هذه الخوارزميات انجزت باستخدام برنامج.Matlab ABSTRACT The objects n the underwater mages are not clearly vsble due to low contrast and scatterng of lght and the large nose present n the envronment whch leads to lose ts orgnal color and detals. The Retnex s an mage enhancement algorthm that mproves the brghtness, contrast and sharpness of an mage. In ths study, we proposed a new mage enhancement algorthm dependent on the Modfed Mult Scale Retnex Algorthm wth Color Restoraton (MMSRCR). Ths technque s usng the lghtness component n YIQ color space that s transformed by the Sgmod Functon and then t s appled n the tradtonal mult scale retnex algorthm MSRCR mage after reveres transformaton. Relatonshp between the mean and the average of standard devaton for mage has been done to examne the effcency of the method. All algorthms has mplemented usng program (Matlab). 1. INTRODUCTION Underwater mage enhancement technques provde a way to mprovng the object dentfcaton n underwater envronment. There s lot of research started for the mprovement of mage qualty, but lmted work has been done n the area of underwater mages, because n underwater envronment mage get blurred due to poor vsblty condtons and effects lke absorpton of lght, reflecton of lght, bendng of lght, denser medum (800 tmes denser than ar) and scatterng of lght. These are the mportant factor whch causes the degradaton of underwater mages [1]. The researchers have revewed several technques related to mages enhancement vz Contrast Stretchng Hstogram Equalzaton Contrast Lmted Adaptve Hstogram Equalzaton (CLAHE). The (MSRCR) s a non-lnear spatal and spectral transform that produces mages that have a hgh degree of vsual fdelty to the observed scene [2]. Here we are gong to the process of testng, developng and extensvely usng the Hstogram Equalzaton (HE), Adaptve Integrated Neghborhood Dependent 511

2 Enhancement of the Underwater Images Usng Modfed Retnex Algorthm Nabeel, Al and Hazm Approach (AINDANE), Multscale Retnex wth Color Restoraton (MSRCR) and Modfy Mult scale Retnex wth Color Restoraton (MMSRCR) algorthms for mage enhancement. 2. Related Works A number of researches have been conducted to study the contrast and lghtness color mage enhancement n dfferent ways, n the followng some of these studes: Foster [3] (2011) dentfed fundamental problems wth defnng color constancy n complex natural vsual envronments and n the same year they proposed an adaptve mult-scale retnex that determnes the sze of the Gaussan flters and correspondng weghts accordng to the ntensty dstrbuton of the nput mage [4]. Hana H. [5] (2012) enhanced the color mages wth dm regons by usng modfy hstogram equalzaton (MHE) algorthm. Ths technque uses the lghtness component n YIQ color space s transformed usng sgmod functon. 3. MATERIALS AND METHODS For the results n ths study, we have selected test color whch s consstng of (red, green, blue, magenta, cyan, yellow, whte and black). After that we sprayng pant on the metallc plate by usng the sprayer dyes, the metallc plate ( cm) connected wth wood pole of length (4.5 m) to controllng of the dstance between the object and camera at dfferent depths as showng n Fgure 1. The vdeo clps have been done by usng underwater camera (Sony DSC-TX10), we used Ulead Vdeo Studo program verson 11 to obtan mages of BMP format wth sze ( pxel); note of that these mages were taken from the swmmng pool (clean water), after that these mages are processed n Matlab. 3.1 Hstogram Equalzaton (HE) Fgure-1: Expermental Setup. A global technque whch works well for a varety of mages s hstogram equalzaton If lghtness levels are contnuous quanttes normalzed to the range (0, 1) and pr(r) refers to the Probablty 512

3 Al- Mustansryah J. Sc. Vol. 24, No 5, 2013 Densty Functon (PDF) of the lghtness levels n a gven mage, where the subscrpt s used for dfferentatng between the PDFs of the nput and output mages. Suppose that the followng transformaton perform on the nput levels get output (processed) ntensty levels [6]. 3.2 AINDANE Algorthm Adaptve Integrated Neghborhood Dependent Approach for Nonlnear Enhancement of Color Images (AINDANE) s an algorthm to mprove the vsual qualty of dgtal mages captured under extremely low or unform lghtenng condtons. It s composed of three man parts: Adaptve Lumnance enhancement, contrast enhancement and color restoraton [5]. 3.3 MSRCR Algorthm The Mult-Scale Retnex (MSR) s explaned from Sngle-Scale Retnex (SSR) that s a combnaton of weghted dfferent scale of SSR, whch gven by [7]: R (x,y,c) = log I (x,y) - log F(x,y,c) I (x,y) (1) where, R (x, y, c) the output of channel ( ϵ R,G,B) at poston x, y, c s the Gaussan shaped surroundng space constant, I (x, y) s the mage value for channel and symbol denoted convoluton. F (x, y, c) Gaussan surrounds functon that s calculated by Eq. 2. ( x 2 y 2 ) [ ] 2 F( x, y, c) k e c (2) F ( x, y, c) dxdy 1 (3) The MSR output s then smply a weghted sum of the outputs of several dfferent SSR output where [8]: R x,y,w,c = W R (x,y,c ) MSR n n n=1 N (4) where, N s the number of scales, R (x, y, cn) the I th component of the n th scale, RMSR (x, y, w, c) the I th spectral component of the MSR output and Wn the weght assocated wth the n th scale. We nsst that ( Wn=1). The result of the above processng wll have both negatve and postve RGB values and the hstogram wll typcally have large tals. Thus a fnal gan-offset s appled as mentoned n [8] and dscussed n more detal below. Ths processng can cause mage colors to go towards gray and thus an addtonal processng step s proposed n [7] to calculate MSR wth color restoraton by Esqs. (5&6): where, I gven by [8]: R' = R *I (x,y,c) (5) MSR ' 513

4 Enhancement of the Underwater Images Usng Modfed Retnex Algorthm Nabeel, Al and Hazm I (x, y,a,b) = b log 1+ a ' 3 I (x, y) I (x, y) =1 where, we have taken the lberty to use log (1 + x) n place of log (x) to ensure a postve result. In [8] a value of 125 s suggested for (a); we emprcally settled on a value of (b = 100) for a specfc test mage. The dfference between usng these two values s small. In eq. (4) a second constant s used whch s smply a multpler of the result. And the fnal step s gan-offset by 0.35 and 0.56 respectvely. The present research uses: (w 1 = w 2 = w 3 = 1/3) and (c 1 = 250, c 2 = 120, c 3 = 80) [9]. 3.4 MMSRCR Algorthm Frst step n ths algorthm s transform color mage from basc RGB color space to YIQ color space, the forward transform s gven by Eq. 7 [8]: y = I = 0.596r g b (7) q = 0.211r g b where, y s lghtness component,,q are chromatc components. In second step s transformed normalzed lghtness value by usng sgmod functon that s gven by Eq.8 [9]: S n = 1/ I I n n where, I n s the normalzed lghtness value that s equal (I/255). Fgure2 shows the relatonshp between nput lghtness n versus output lghtness S n. (6) (8) Fgure-2:Relatonshp between nput lghtness versus output lghtness. Then used the nverse transformaton from YIQ to RGB color space calculated n YpIQ that s gven by [8]: r p = y p q g p = yp q b p = yp q (9) 514

5 Al- Mustansryah J. Sc. Vol. 24, No 5, 2013 Fnally the MSRCR had been appled on new component n Eq Image Qualty Assessment Some researchers n NASA Langley research center studed regonal means (vsual lghtness) and standard devatons (vsual contrast) and found that they tend to converge on consstent global aggregates [10]. Ths mples that a good vsual representaton can be assocated wth well-defned statstcal measures for vsual qualty. In scentfc terms, ths mples the exstence of a canoncal vsual mage as a statstcal practcal deal. Such a defned deal can then serve as the bass for the automatc assessment of vsual qualty. To compute the regonal parameters, we dvde the mage nto non overlappng pxel blocks. For each block, a mean I b and a standard devaton σ b are computed. Ths regonal scale s suffcently granular to capture the vsual sense of regonal brghtness and contrast. Both the global contrast and lghtness can then be measured n terms of the regonal parameters. The overall lghtness s measured by the mage mean µ= I b, whch s also the ensemble measure for regonal lghtness. The overall contrast s measured by takng the mean of regonal standard devatons b as shown n Fgure 3 and t provdes a gross measure of the regonal contrast varatons. A classfcaton of excellent, good, or poor s then based on how many of these regonal blocks exceed a gven contrast and brghtness threshold. 5. RESULTS DISSCUSSIONS We study new ways to control ntensty of lght and color for the purpose of capturng better qualty data n poor vsblty envronments. The mages whch used n the present study have been llustrated n Fgure 4. These mages were enhanced by usng dfferent algorthms as shown n Fgs. (5-8), the relatonshp between mean of local standard devaton and mean for mage t s llustrated n Fgure 9 a-g the ponts of MMSRCR tend to vsual optmal regon. In fact, ths algorthm was enabled to ncrease both lghtness and contrast n mage whch compared wth other ponts. We can conclude that the MMSRCR s robust method to enhance color-mage wth degraded lghtness levels. 6. Conclutons A computaton lke the MMSRCR appears to have two very useful propertes smultaneously: a dmnshment n the dependence of the appearance of the mage on poor vsblty envronments and extraneous varables, such as spatal spectral and lghtng. The former s nherently useful because t can lead to better mage classfcatons and the latter because t shows very clearly that the appearance of a color s dependent not only on the spectral characterstcs of a pxel, but also ts 515

6 Enhancement of the Underwater Images Usng Modfed Retnex Algorthm Nabeel, Al and Hazm surround. Together these propertes may be able to provde a bass for brngng more advanced levels of vsual ntellgence nto computng. Fgure-3: Image qualty descrpton [10]. Fgure-4: Orgnal mages (a) n ar, (b) n depth 50 cm underwater, (c) n depth 100 cm underwater, (d) n depth 150 cm underwater, (e) n depth 200 cm underwater, (f) n depth 250 cm underwater. Fgure-5: Enhanced mages by usng MMSRCR algorthm (a) n ar, (b) n depth 50 cm underwater, (c) n depth 100 cm underwater, (d) n depth 150 cm underwater, (e) n depth 200 cm underwater, (f) n depth 250 cm underwater. Fgure- 6: Enhanced mages by usng HE (a) n ar, (b) n depth 50 cm underwater, (c) n depth 100 cm underwater, (d) n depth 150 cm underwater, (e) n depth 200 cm underwater, (f) n depth 250 cm underwater. 516

7 Al- Mustansryah J. Sc. Vol. 24, No 5, 2013 Fgure-7: Enhanced mages by usng MSRCR algorthm (a) n ar, (b) n depth 50 cm underwater, (c) n depth 100 cm underwater, (d) n depth 150 cm underwater, (e) n depth 200 cm underwater, (f) n depth 250 cm underwater. Fgure-8: Enhanced mages by usng AINDANE algorthm (a) n ar, (b) n depth 50 cm underwater, (c) n depth 100 cm underwater, (d) n depth 150 cm underwater, (e) n depth 200 cm underwater, (f) n depth 250 cm underwater. Fgure -9:Relatonshp between mean of local standard devatons and mean of orgnal and enhanced mages (a) n ar, (b) n depth 50 cm underwater, (c) n depth 100 cm underwater, (d) n depth 150 cm underwater, (e) n depth 200 cm underwater, (f) n depth 250 cm underwater. 517

8 Enhancement of the Underwater Images Usng Modfed Retnex Algorthm Nabeel, Al and Hazm 7. REFERENCES 1. Sngh, B., R.S. Mshra and P. Gour," Analyss of Contrast Enhancement Technques for Underwater Image. Int. J. Comput. Technol. Elect. Eng., Vol. 1, pp , Anjal C., Bbhudendra A. and Mohammad I. K.," Retnex Image Processng: Improvng the Vsual Realsm of Color Images", Int. J. Inform. Technol. Knowledge Management, Vol. 4, No. 2, pp , Foster, D. H., "Color Constancy" Vson Research Vol. 51, No.7, pp , In-Su J., Tae-Hyoung L., Wang-Jun K., and Yeong-Ho H., Local Contrast Enhancement Based on Adaptve Mult-Scaled Retnex usng Intensty Dstrbuton of Input Image. J. of Imagng Sc. and Technol. 55(4): , Socety for Imagng Scence and Technology Hana H. K. Color mage wth Dm regons Enhancement Usng Modfed Hstogram Equalzaton Algorthm. J. of Al-Nahran Unv., Vol.15 No.3, September, pp , Gonzales R. C. and Woods R. E., "Dgtal Image Processng Readng", MA: Addson-Wesley, Jobson, D.J., Z. Rahman and G.A. Woodell, "Propertes and Performance of a Center/Surround Retnex", IEEE Trans. Image Proc., Vol. 6, pp , Jabson, D.J., Z. Rahman and G.A. Woodel, "A Mult-scale Retnex for Brdgng the Gap Between Color Images and the Human Observaton of Scenes", IEEE Trans. Image Proc., Vol. 6, pp , Gupta, M., S.G. Narasmhan and Y.Y. Schechner, "On Controllng Lght Transport n Poor Vsblty Envronments", Proceedngs of the IEEE Conference on Computer Vson and Pattern Recognton, Jun , IEEE Xplore Press, Anchorage, AK,p.p:1-8, Jabson, D.J., Z. Rahman and G.A. Woodell, "Statstcs of Vsual Representaton", Proceedngs of the SPIE 4736, Vsual Informaton Processng, Jul , pp: 25-35,