Outline. Image processing includes. Edge detection. Advanced Multimedia Signal Processing #8:Image Processing 2 processing

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1 Outlin Advancd Multimdia Signal Procssing #8:Imag Procssing procssing Intllignt Elctronic Sstms Group Dpt. of Elctronic Enginring, UEC aaui agai Imag procssing includs Imag procssing fundamntals Edg dtction and filtring Dnoising Imag rsizing Imag nlargmnt Supr-rsolution MAP stimation Imag procssing includs Edg dtction Qualit nhancmnt brightnss contrast color adjustmnt rotation rsiz dnoising Comprssion coding JPEG MPEG- MPEG-4 H.64 Snthsiz Computr graphics Rcognition Charactr rcognition OCR Objct rcognition Robot Computr vision 3D rconstruction tc. dg a sharp discontinuit in gr-lvl profil dgs carr almost all of visual information w actuall s dgs! imag drivativ imag filtring 3 4

2 Drivativs Drivativs cont d Drivativs horizontal and vrtical dirctions f, f, f, f, f, f, f, f, f, f, drivativs at, smmtric diffrnc vrtical dg f, f, [ f, f, ] [ f, f, ] 6 horizontal dg -? Oprator ampls drivativ smoothing to cop with nois diffrnt smoothing mthod 3 3 is commonl usd prwitt drivativ smoothing prwitt smoothing drivativ 7 sobl bttr smoothing sobl

Scond drivativ Eampl of th scond drivativ diffrnc of th diffrnc { f, f, } { f, f, } { f, f, } { f, f, } f, laplacian 4 nighbors f, -4 f, laplacian 8 nighbors f, 4 f, -8 Laplacian 4 nighbors zro cross

3 Scond drivativ Eampl of th scond drivativ diffrnc of th diffrnc { f, f, } { f, f, } { f, f, } { f, f, } f, laplacian 4 nighbors f, -4 f, laplacian 8 nighbors f, 4 f, -8 Laplacian 4 nighbors zro cross 55 whit blac 9 horizontal and vrtical diagonal dirction this imag has bn procssd so that th pil valus ar in th rang of to 55 Edg dtction Cann dg dtctor Finall w hav to judg whthr th pil is dg or not various mthods thrsholding p, prwitt _ f, prwitt _ f, thrshold dg p, < thrshold no dg Zro-cross dtction as to find too man dgs might b found bcaus of nois hrsholding blurrd b th Gaussian filtr nois rmoval LOG filtr Laplacian Of Gaussian filtr popular mthod good prformanc Gaussian filtring nois rmoval drivativs magnitud and orintation G G G G θ tan G hinning nonmaimum supprssion using orintation info. multipl thrsholds uppr and lowr tracing dgs starting from an dg with high confidnc and conncting dgs with low confidnc

4 hor of imag procssing DF 3 Imag dimnsional signal Vido 3 dimnsional signal -D signal procssing thor is applicabl to ths signals-d -D, 3-D Sampling thorm, frqunc, DF, Z-transform, convolution, transfr function, impuls rspons, filtr horizontal and vrtical dirctions h oprator for dg dtction can b viwd as a digital filtr -D filtr Discrt Fourir ransform X n πn j n n forward transform π j π π cos j sin n X n n n X πn j X inv. transform n,, L In ordr to appl th DF to D imags ind coordinat 4 n, imag plan u,v spatial frqunc -D Discrt Fourir ransform Graphical planation 5 F u, v f, Forward f, pil val at, width hight F u, v f, f, u v jπ F u, v u v jπ u jπ v Invrs f, v j π u jπ horizontal DF of vrtical DF 6 DF j 7j - - -j j j -j 7 j 7 7j DF DC rarrang ths so that th DC is on th cntr of th plan low 4 6 j -8 -j nquist frqunc high -3-35j j 3j j -3j 5 j 5 -j -8 3j -j -3j

m 7 4 6 j -8 -j -3-35j -8-3 35j 3j j -3j 5 j 5 -j -8 3j -j -3j 5 -j -8 -j -3j -3 6 35j -8 j -3j -8 -j 4 j 5 j 3j -3-35j 3j 8 Discrt tim

5 Rarrangmnt Eampl of -D DF DC 4 3 DC 3 4 low high high Ximrad lnna.bmp ; FfftdoublX/55; FfftshiftF; %displa mans nothing imshowabsf/ %load imag %-D DF %FF shift imagff.m j -8 -j -3-35j j 3j j -3j 5 j 5 -j -8 3j -j -3j 5 -j -8 -j -3j j -8 j -3j -8 -j 4 j 5 j 3j -3-35j 3j 8 Discrt tim sstm Oprator as a filtring dimnsional sstms f, input sstm, R[ f, ] output scanning th masoprator lin b lin ovr th whol imag filtring! dimnsional convolution R[ ] Frqunc rspons of Prwitt oprator? -D sstm Each sstm can b rprsntd b th impuls rspons

Frqunc rspons Dnoising z n prwitt FIR filtr - - - z n H z z z z z z z z

nois dg-prsrving smoothing nonlinar filtr ran-ordr filtr z n - z n - H

6 Frqunc rspons Dnoising z n prwitt FIR filtr z n H z z z z z z z z smoothing filtr LPF stationar nois mdian filtr nonlinar filtr impulsiv nois dg-prsrving smoothing nonlinar filtr ran-ordr filtr z n - z n - H z z z z z z z z nois: salt&pppr 33 avrag 33 mdian Zoom out th imag Zoom in th imag cop ach pil... dcimation down-sampling LPF s handout #3 information loss nd to fill in th blans intrpolation similar to th D cas 3 4

$applicabl avrag of 4 nighbors a b 5 Linar intrpolatd imag intrpolatd imag Original imag 6 c q p -q -p d pi_valu-p{-qa qb} p{-qcqd}$ a,b,c,d: obsrvd pil valus Supr Rsolution Low rsolution imag gnration modl multi-imag intgration for th imag rsolution nhancmnt subpil

a,b,c,d: obsrvd pil valus Supr Rsolution Low rsolution imag gnration modl multi-imag intgration for th imag rsolution nhancmnt subpil

7 arst ighbor mthod pand ach pil Linar intrpolation Avraging th surrounding pils Up-samplr intrpolation filtr highr ordr filtrs ar applicabl avrag of 4 nighbors a b 5 Linar intrpolatd imag intrpolatd imag Original imag 6 c q p -q -p d pi_valu-p{-qa qb} p{-qcqd} a,b,c,d: obsrvd pil valus Supr Rsolution Low rsolution imag gnration modl multi-imag intgration for th imag rsolution nhancmnt subpil shiftd pil valus ar obsrvd to rconstruct th high rsolution imag DB M n n 7 Problm 8 stimat from th obsrvations is assumd to b nown or stimatd simultanousl

9 ML stimation zro man Gaussian nois covarianc matri C P p Σ us stpst dscnt mthod to solv th problm Σ stimat SR which maimizs th following lilihood P argma argma Σ minimization problm quadratic form

8 9 ML stimation zro man Gaussian nois covarianc matri C P p Σ us stpst dscnt mthod to solv th problm Σ stimat SR which maimizs th following lilihood P argma argma Σ minimization problm quadratic form I Σ σ 3 Rgularization ML stimation wors good if thr ar nough numbr of imags othrwis, no invrs matri ists numricall unstabl rgularization I α ill-posd problm 3 MAP stimation maimiz th a postriori probabilit Maimum A Postriori { } ln ln argma argma P P P lilihood prior rror btwn and is assumd to b i.i.d. Gaussian smoothnss constraint is applicabl to natural imags as a priori information output from HPF tnd to b small C α Laplacian can b usd as C 3 two tims imag zooming in ach dirction using 4 imags a intrpolation bmlrgularization small cmlrgularizationbig dmap

Outline. Why speech processing? Speech signal processing. Advanced Multimedia Signal Processing #5:Speech Signal Processing 2 -Processing-

Outline. Why speech processing? Speech signal processing. Advanced Multimedia Signal Processing #5:Speech Signal Processing 2 -Processing- Outlin Advancd Multimdia Signal Procssing #5:Spch Signal Procssing -Procssing- Intllignt Elctronic Systms Group Dpt. of Elctronic Enginring, UEC Basis of Spch Procssing Nois Rmoval Spctral Subtraction