L. Yaroslavsky. Image data fusion. Processing system. Input scene. Output images

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1 L. Yaolavk Image daa fuion Poceing em Inpu cene Oupu image

Muli componen imaging and eoaion model Conide an M componen imaging em and aume ha each image of M componen can be decibed in a domain of a ceain ohogonal anfom b he model ( m ) ( m ) ( m ) ( m ) { β

2 Muli componen imaging and eoaion model Conide an M componen imaging em and aume ha each image of M componen can be decibed in a domain of a ceain ohogonal anfom b he model ( m ) ( m ) ( m ) ( m ) { β = λ α ν } ( m ) ( m ) { } β { } α whee m i componen indem= M and ae pecal coefficien of he obeved and pefec image ( m ) ( m ) componen { λ } ae componen linea anfomaion pecal coefficien and { ν } ae addiive noie pecal coefficien in m-h componen. Suppoe ha image componen ae eoed b a linea combinaion of cala fileed inpu image componen: M ( m ) ( m l ) ( l ) ˆ α = η β l= ( m l ) whee { η } ae eoaion cala linea file coefficien. Tanfom Poceing em Invee Tanfom Tanfom Invee Tanfom Oupu image Inpu cene Tanfom M l= η ( m l ) ( l ) β Invee Tanfom

3 MSE-opimal muli componen cala fileing L.P.Yaolavk H.J. Caulfield Deconvoluion of Muliple Image of he ame objec Appl. Op. Vol. 33 No p. 57- L. Yaolavk M. Eden Coelaional Accumulaion a a Mehod fo Signal Reoaion Signal Poceing 39 (994) p MSE-cieion of poceing quali ˆ α ( m ) N M = ag min AV Ω AV Α ΩΝ { ( )} ˆ α η m l η = 0 l = ( m ) ( m l ) ( l ) Aveaging ove an image enemble Aveaging ove an image dioion enemble β MSE-opimal eoaion file coefficien Special cae: η ( m l ) λ ( l ) ( m ) ( l ) ( α α ) ( l ) ( α ) Ω A = M AV AV Ω A SNR m= ( l ) SNR ( m ) whee SNR ( m ) = λ ( ) Ω α A ( m ) ( ) ν ( m ) ( m ) AV AV Ωn. Aveaging of muliple image of he ame objec obained wih he ame eno: ( m ) ( m ) ( m ) bk / σ n whee { σ n } ae andad deviaion of uncoelaed addiive noie in image componen. Fouie domain image alignmen and aveaging : whee in Fouie Tanfom bai ˆ α ( m ) M l= = M u ep iπ l= ( m l ) u ep iπ N / σ β () l n () l () l / σ n ( α α ) ( m l ) ( m ) ( l ) AVΩ A ( ) N = l AVΩ ( α ) A aˆ M m= k = M m= / σ ( m ) n

4 Cae ud: Sabilizaion and upeeoluion in ubulen video

5 Amopheic ubulence image dioion model MapY MapX lcmapping_abi(len(mapxi*mapy)/.588)

6 Sabilizaion of video dioed b amopheic ubulence: he algoihm flow cha Inpu video equence Fomaion of a efeence fame (piel wie empoal median) Deeminaion of local mialignmen paamee ( opical flow ) Deecion of moving objec Fame eampling/alignmen ( elaic egiaion) Sabilized movie (opional: upeeoluion image)

7 Deeminaion of local diplacemen Two opion Opical flow algoihm: Block maching algoihm Obeved fame Refeence fame

8 Opical flow mehod fo finding image local mialignmen paamee ( ) ( ) [ ) ( ) ( ) ( ) ( ) ( ag min Δ Δ Δ Δ Ω Δ Δ a d da d d da a a Fi-ode uncaed Talo epanion ( ) ( ) = NBH ef ef ef ef ef ef a a ) ( ) ( ag min Δ Δ Δ Δ Δ Δ Δ

9 Deecion of moving objec b mean of clueing movemen veco Obeved fame hehold Moion mak

10 Sabilized video

11 Tubulence compenaion and abilizaion of video Inpu video Sabilized video

12 Tubulence compenaion and abilizaion of video

13 Supe-eoluion in Tubulen Video Random ampling wih low pa fileing Elaic egiaion and daa accumulaion Inepolaion of pae daa

Ieaive algoihm fo dicee inc-inepolaion of pae daa

pecum (DFT/DCT) Image pecum limiaion Invee Tanfom

image Eample of ecoveing miing image ample.

5 (miing piel ae e o zeo). Righ column eoed image.

14 Ieaive algoihm fo dicee inc-inepolaion of pae daa Inpu image (fi ieaion) ieaed image Compuing image pecum (DFT/DCT) Image pecum limiaion Invee Tanfom (IDFT/IDCT) Reoaion of available image ample eoed image Eample of ecoveing miing image ample. Lef column dioed image pobabili of miing daa 0.5 (miing piel ae e o zeo). Righ column eoed image. Fom op o boom: iolaed miing ipe-5 pae_ampl_incin_demo;

15 Tubulen video abilizaion and upeeoluion Inepolaed Image Supe-eolved Image

16 MPEG-4 Supe-eoluion fom Global Moion Fi fame inepolaed Supe-eolved fame

17 Cae ud: Themal and viual ange daa fuion Themal channel Viual ange channel Simple miue of he channel

18 IR and viual ange video daa fuion

19 Sliding window 3-D Empiical Wiene fileing fo video eoaion ( ) ( ) ( ) p p p ˆ β η α = ( ) ( ) ( ) = β AV β 0 ma p p p λ ν η ( ) ( ) op SNR SNR AV AV AV = = λ ν α α λ λ η = = ˆ ˆ agmin AV ˆ N id a a a a ( ) ( ) ( ) = = ˆ ˆ ag min ˆ N p id p AV p α α α α Imaging model { } a A = - veco of ample of undioed video N A H B = -veco of ample of dioed video { } b B = { } n N = -veco of addiive noie ample H - linea opeao MSE-appoach: Ohogonal anfom model: ν α λ β = Scala fileing: MSE-opimal (Wiene) cala file Empiical Wiene file ( of heholding) Rejecing file ( had heholding) ( ) ( ) = AV β ign 0 ma p p ν λ η { } a ) - ample of eoed video Sliding 3D window cala fileing Invee anfom fo he window cenal piel Scala file Sliding 3D window Local pecum Modified pecum Inpu fame Oupu fame

20 Sliding window D and 3-D DCT domain empiical Wiene fileing fo video eoaion Compue imulaion: ideal noi ideal noi -D 3-D -D 3-D

21 3-D Local adapive paial-empoal fileing: denoiing and debluing hemal video Real life IR ange video Poceed video wih blind debluing (555 DCT domain fileing)

22 Spaial/empoal fileing fo noie uppeion in Themal Band Video

23 Weighed Aveage Piel Level Baed Image Ine-channel Fuion I I Fued k l IR k l IR image ( w ( w = VI IR k l VI IR k k Viual impoance weigh w w Viible ange image SNR IR k l SNR IR k l w w Moion IR k l Moion IR k l Noie level weigh ) I ) ( w IR image ( w Vi VI Vi k l k l VI Vi k l w w SNR Vi k l SNR Vi k l Viible ange image Moion conolled weigh w w Moion Vi k l Moion IR k l ) )

Viual impoance weigh Weigh ae evaluaed in local neighbohood Fo hemal Image elemen ha ae wame o coole han hei backgound ae o be enhanced Fo he Viual Image aea wih highe local vaiance ae

24 Viual impoance weigh Weigh ae evaluaed in local neighbohood Fo hemal Image elemen ha ae wame o coole han hei backgound ae o be enhanced Fo he Viual Image aea wih highe local vaiance ae o be enhanced w median IR IR { } IR VI Vi Vi l I k l MEDN I k l LocWindow w { I } VI k k l i j LocWindow STD The cemen poll in he bick wall and he ho-po in he field ae much bee noiceable

25 Noie level weigh Local auocoelaion Noie local vaiance eimae ( Window ) SNR IR w k l = NoieVaiance k l Sliding window Image fuion uing boh Viual Impoance weigh and noie level-weigh

26 Fuion uing moion veco weigh Fame of IR video Fame of Viual Range video Fuion wihou moion conolled weigh Moion veco Fuion uing moion conolled weigh

27 Refeence L. Yaolavk M. Eden Coelaional Accumulaion a a Mehod fo Signal Reoaion Signal Poceing 39 (994) pp L.P.Yaolavk H.J. Caulfield Deconvoluion of Muliple Image of he ame objec Applied Opic Vol. 33 No 0 Apil 994 p L. Yaolavk Digial Hologaph and Digial Image Poceing Kluwe Academic Publihe Boon 004 L. Yaolavk B. Fihbain G. Shaba I. Idee Supe-eoluion in ubulen video: making pofi fom damage Opic Lee Nov. 007 Vol. 3 No. B. Fihbain L. P. Yaolavk I. A. Idee Real Time Sabilizaion of Long Range Obevaion Sem Tubulen Video Jounal of Real Time Image Poceing Volume Numbe / Ocobe 007 B. Fihbain. L. Yaolavk I. Idee Spaial Leonid P. Yaolavk "Spaial Tempoal and Inechannel Image Daa Fuion fo Long-Diance Teeial Obevaion Sem" Advance in Opical Technologie vol. 008 B. Fihbain L. Yaolavk I. Idee Real Time Tubulen Video Supe-Reoluion Uing MPEG-4 SPIE Conf. Eleconic Imaging San Joe Jan. 008 Poceeding of SPIE Vol. #68

Lecture 26: Leapers and Creepers

Lecture 26: Leapers and Creepers Lecue 6: Leape and Ceepe Scibe: Geain Jone (and Main Z. Bazan) Depamen of Economic, MIT May, 5 Inoducion Thi lecue conide he analyi of he non-epaable CTRW in which he diibuion of ep ize and ime beween