A O ti i d R i b d C l T fAn Optimized Region-based Color TransferAn Optimized Region based Color TransferM th d f Ni ht Vi i A li tiMethod for Night Vision ApplicationMethod for Night Vision Applicationg pp

Tanish Zaveri Mukesh Zaveri Ishit Makwana and Harshit MehtaTanish Zaveri, Mukesh Zaveri, Ishit Makwana and Harshit Mehta

Introduction P bl St t tIntroduction Problem Statement The general problem of colorizing a grayscale image has no exact and

Modern night vision systems are designed to expand the conditions under The general problem of colorizing a grayscale image has no exact and

particular solution Modern night vision systems are designed to expand the conditions under which human observers can operate

particular solution. The challenge is to give nightvision imagery an intuitively meaningfulwhich human observers can operate.

Ni ht ti i i t id l t i f ti f th The challenge is to give nightvision imagery an intuitively meaningful

(" t li ti ") d t bl l t i th i Night-time imaging systems provide complementary information of the ("naturalistic") and stable color appearance, to improve the viewers h i d h bj i i & iinspected scene in the form of Visible and Infrared (IR) images. scene comprehension and enhance object recognition & segmentation.

Color imaging modes in night vision systems have become an important g g g y pdeveloping direction. Objective:p g

Natural color representation of fused night-time imagery will help the observer

jTo impart natural daytime colors to the grayscale fused NV images by an Natural color representation of fused night time imagery will help the observer

by making scene interpretation more intuitive resulting in more situational

p y g y g yoptimized region-based approach.by making scene interpretation more intuitive, resulting in more situational

awareness faster reaction time and more accurate object identification

optimized region based approach.

awareness, faster reaction time and more accurate object identification.

Lit t SLiterature SurveyyGeneral Classification of Natural Color Mapping Approaches forpp g ppNightvision Colorization is as follows:Nightvision Colorization is as follows:1 Statistics Transfer based techniquesProposed Method 1. Statistics Transfer based techniques2 Pattern matching based color transfer techniques

Proposed MethodFig re 1 Nat ral Color Mapping for 2. Pattern matching based color transfer techniques

3 Region based natural color mapping techniquesBasic Algorithms UsedFigure 1. Natural Color Mapping for

M ltib d Ni ht i i I 3. Region based natural color mapping techniquesBasic Algorithms Used Multiband Nightvision Imagery.

Pseudo-color Fusion Pseudo-color Fusion C l b d I S t ti Color-based Image Segmentation Non-linear Diffusion Non linear Diffusion Hill Climbing algorithm for color based segmentation Hill Climbing algorithm for color-based segmentation

Grayscale Image Fusion based on Fuzzy Region Featurey g y g Region Recognition and Color Transfer Region Recognition and Color Transfer.

Pseudo-color Fusionseudo colo usio Improves efficiency of segmentation & visually coherent regions can be Improves efficiency of segmentation & visually coherent regions can bebt i d b i ti f l lobtained by imparting false colors. Hybrid high boost filter based method [6]Here, F is pixel-based average between visible and IR image. The hybrid high, p g g y gboost filtered image is - .boost filtered image is .

A = W AAhb = Whb . Aoriginal

dandBhb = Whb . Boriginalhb hb original

where Aoriginal and Boriginal are Visible and IR images respectively and Whb is a high Figure 2. Block Diagram of Proposed Method.where Aoriginal and Boriginal are Visible and IR images respectively and Whb is a highboost convolution kernel given by

Figure 2. Block Diagram of Proposed Method.boost convolution kernel given by,

W = α W + WWhb = α . Wallpass + Whighpass

H 0 i h i f d d d i i f IR i h d d d i i f Simulation ResultsHere α > 0 is the ratio of standard deviation of IR image to the standard deviation of Simulation Resultsvisible image. {Ahb,F,Bhb} are put to the three color channels of RGB model, respectively, and{ hb, , hb} p , p y,

thus pseudo-colored image is generated.thus pseudo colored image is generated.

C l b d S iColor based Segmentation Non-linear Diffusion [5][ ] Preserves the edges and smoothes the similar regions Preserves the edges and smoothes the similar regions. Smoothes the regions with lower gradients and retains the boundaries with Smoothes the regions with lower gradients and retains the boundaries with

hi h di thigher gradients. Enables the extraction of dominant colors in the natural color image.

Hill Climbing algorithm for color-based image segmentation [3] Hill Climbing algorithm for color based image segmentation [3] Produces an optimized set of visually coherent regions Produces an optimized set of visually coherent regions. Fi t l l i i bt i d f th l t i th 3D l hi t f First, a local maxima is obtained of the clusters in the 3D color histogram of

the image in lαβ color space. Pixels of the image are associated with the detected local maxima to produce Figure 3. Obtaining pseudo-colored segmented image for set of Figure 4. Color Transfer results for Camp NV Images. Theg p

the segmented regions.g g p g g

Camp NV images.Figure 4. Color Transfer results for Camp NV Images. The results are compared with the method given by Pitie et al [7]t e seg e ted eg o s.

Segmentation is performed using K-means algorithm in which K seeds areCamp NV images. results are compared with the method given by Pitie et al [7]

Segmentation is performed using K-means algorithm in which K seeds areautomatically determined by hill climbing algorithmautomatically determined by hill climbing algorithm.

Th h l t ti i f d t ti ll d i ti i d The whole segmentation process is performed automatically and is optimizedwithout any user intervention.

Grayscale Image FusionGrayscale Image Fusion F R i F t b d F i f Vi ibl & IR i [Li ] Fuzzy Region Feature based Fusion of Visible & IR image [Liu] A region based method which focuses on pixel gray level distribution in the

image region in order to preserve the region feature of source imagesg g p g g Discrete Wavelet Frame Transform on both source images and the low Discrete Wavelet Frame Transform on both source images and the low

frequency bands are segmented into important regions sub-important regionsfrequency bands are segmented into important regions, sub-important regionsand background regions by using fuzzy logicand background regions, by using fuzzy logic

R i b d f i i f d i f Region based fusion is performed in fuzzy space Preserves contrast and obtains good region similarity.

Region Recognition & Color Transfer Fi 5 Obt i i d l d t d i f t fRegion Recognition & Color Transfer T bl l ti l t f f t t i

Figure 5. Obtaining pseudo-colored segmented image for set of Figure 6. Color Transfer results for Trees NV Images. The To enable selective color transfer from target image. Trees NV images. results are compared with the method given by Pitie et al [7] First order statistics in the intensity component of each region is considered for

region recognition, in the HSV color space.g g p Each region in source and target image is characterized by standard deviation of Discussion & Conclusion Each region in source and target image is characterized by standard deviation of

intensityDiscussion & Conclusion

intensity. Corresponding to each region in target image one region of source image is Corresponding to each region in target image, one region of source image is

i t d b d th t l f t d d d i ti Hill-climbing algorithm optimizes the performance of the fuzzy region feature based image fusion method and color

associated based on the nearest value of standard deviation. transfer method. Thus it generates an optimized natural colored Nightvision image. The H and S values are transferred from the target region to the source region in Images obtained by the proposed color transfer method preserves natural color in nightvision imagery which helps the

a pixel-based transfer. Images obtained by the proposed color transfer method preserves natural color in nightvision imagery which helps the

human/machine for better classification and situational awarenessp Finally, HSV to RGB transform is performed to obtain the Colorized

human/machine for better classification and situational awareness. The proposed method has better visual quality so it enhances the information content of the multiband NV source Finally, HSV to RGB transform is performed to obtain the Colorized

Nightvision Image The proposed method has better visual quality so it enhances the information content of the multiband NV source

i hi h lt i hi h t l lit d b tt i t t ti f thNightvision Image. images, which results in high perceptual quality and better interpretation of the scene.

Lit t Cit dLiterature CitedFor Further Information Contact[1] Toet A., Natural Colour Mapping for Multiband Nightvision imagery. Information Fusion, 4(3):155-166, 2003.

[2] Zh Y E k E A A L l C l i h d f i h i i l i i ili i i l i d f i I f i F i 9 186 U199 A il 2008 For Further Information Contact…[2] Zheng Y., Essock, E. A., A Local-Coloring method for night-vision colorization utilizing image analysis and fusion. Information Fusion, 9:186 U199, April 2008.[3] Ohashi T., Aghbari, Z., Makinouchi, A., Hill-climbing algorithm for efficient color-based image segmentation. IASTED International Conference on Signal

Prof Tanish ZaveriProcessing, Pattern Recognition and Applications, pp. 17-22, July 2003.[4] Yheng Z., Song, J., Zhou, W., Wang, R., False color fusion for multiband SAR images based on contourlet transform. ACTA Automatica Sinica, vol. 33(4), April Prof. Tanish Zaveri,

Image Processing Lab

[ ] g , g, , , , g, , g , ( ), p2007.[5] Weickert M V J Romeny B M H Efficient and reliable schemes for nonlinear diffusion filtering IEEE Transactions on Image Processing vol 7 pp 398-410 Image Processing Lab,

Department of Electronics & Communication Engineering,

[5] Weickert M. V. J., Romeny, B.M. H., Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Transactions on Image Processing, vol. 7, pp. 398 410, 1998.[6] Liu S S y Gang JING Zhong liang Multiresolution image fusion scheme based on fuzzy region feature Zhejiang University Science and Technology vol 7(2) Department of Electronics & Communication Engineering,

Institute of Technology,[6] Liu S. S.-y. Gang, JING Zhong-liang. Multiresolution image fusion scheme based on fuzzy region feature. Zhejiang University - Science and Technology, vol. 7(2), pp. 117-122, February 2006.[7] Pi i F K k A D h R A d l di i l di ib i f C Vi i d I U d di (2007)

gyNirma University, Ahmedabad.

[7] Pitie F., Kokaram, A., Dahyot R., Automated colour grading using colour distribution transfer. Computer Vision and Image Understanding, (2007).[8] Night vision multiband source images data set [online] available: http://www.imagefusion.org.

Email: ztanish@ieee.org.