Guided Image Filtering

Transcription

1 Guded Image Flterng Kamng He Jan Sun Xaoou Tang The Chnese Unversty of Hong Kong Mcrosoft Research Asa The Chnese Unversty of Hong Kong

2 Introducton Edge-preservng flterng An mportant topc n computer vson Denosng, mage smoothng/sharpenng, texture decomposton, HDR compresson, mage abstracton, optcal flow estmaton, mage superresoluton, feature smoothng Exstng methods Weghted Least Square [Lagendj et al. 1988] Ansotropc dffuson [Perona and Mal 1990] Blateral flter [Aurch and Weule 95], [Tomas and Manduch 98] Dgtal TV (Total Varaton) flter [Chan et al. 2001]

3 Introducton Blateral flter q W jn ( ) j ( p) p j spatal G s (x -x j ) nput p range G r (p -p j ) blateral W=G s G r output q

4 Introducton Jont blateral flter [Petschngg et al. 2004] q W jn ( ) j ( I) p j blateral flter: I=p spatal G s (x -x j ) nput p blateral W=G s G r output q range G r (I -I j ) gude I E.g. p: nosy / chromnance channel I: flash / lumnance channel

5 Introducton Advantages of blateral flterng Preserve edges n the smoothng process Smple and ntutve Non-teratve

6 Introducton Problems n blateral flterng Complexty Brute-force: O(r 2 ) Dstrbutve hstogram: O(logr) [Wess 06] Blateral grd: band-dependent [Pars and Durand 06], [Chen et al. 07] Integral hstogram: O(1) [Porl 08], [Yang et al. 09] Approxmate (quantzed)

7 Introducton Problems n blateral flterng Complexty Gradent dstorton Example: detal enhancement gradent reversal Preserves edges, but not gradents gradent reversal nput enhanced

8 Introducton Our target - to desgn a new flter Edge-preservng flterng Non-teratve O(1) tme, fast and non-approxmate No gradent dstorton Advantages of blateral flter Overcome blateral flter s problems

9 Guded flter q p n mn ( a, b) ( ai b p ) 2 a 2 nput p n - nose / texture Lnear regresson gude I q q a I ai b output q Blateral/jont blateral flter does not have ths lnear model a b cov( I, p) var( I) p ai

10 Guded flter Defnton Extend to the entre mage In all local wndows ω,compute the lnear coeffcents a b cov ( I, p) var ( I) p ai Compute the average of a I +b n all ω that covers pxel q q 1 ( a I b ) q a I b ω 2 ω 1 ω 3

11 Guded flter Defnton Parameters Wndow radus r Regularzaton ε a b cov ( I, p) var ( I) p ai q 1 ( a I b ) q a I b ω 2 ω 1 2r ω 3

12 Guded flter: smoothng a cascade of mean flters a b cov( I, p) var( I) p ai var( I) cov( I, p) a b 0 p q ai b p nput p output q gude I var(i) r : determnes band-wdth (le σ s n BF)

13 Guded flter: edge-preservng q ai b q ai I a b a cov( I, p) var( I) ε : degree of edge-preservng (le σ r n BF) I q gude I output q

14 Example edge-preservng smoothng nput & gude guded flter (let I=p) r=4, ε=0.1 2 r=4, ε=0.2 2 r=4, ε=0.4 2 blateral flter σ s =4, σ r =0.1 σ s =4, σ r =0.2 σ s =4, σ r =0.4

15 Our target - to desgn a new flter Edge-preservng flterng Non-teratve O(1) tme, fast and non-approxmate No gradent dstorton Advantages of blateral flter Overcome blateral flter s problems

16 Complexty mean, var, cov n all local wndows Integral mages [Franln 1984] O(1) tme ndependent of r Non-approxmate a b q Defnton cov ( I, p) var ( I) p a I ai b O(1) blateral (32-bn, 40ms/M) [Porl 08] O(1) blateral (64-bn, 80ms/M) O(1) guded (exact, 80ms/M)

17 Gradent Preservng blateral flter guded flter nput fltered q ai detal (nput - fltered) large fluctuaton enhanced (detal * 5 + nput) gradent reversal

18 Example detal enhancement gradent reversal blateral flter guded flter nput (I=p) blateral flter σ s =16, σ r =0.1 guded flter r=16, ε=0.1 2

19 Example detal enhancement gradent reversal blateral flter guded flter nput (I=p) blateral flter σ s =16, σ r =0.1 guded flter r=16, ε=0.1 2

20 Example HDR compresson nput HDR gradent reversal blateral flter eep ant-alased guded flter blateral flter σ s =15, σ r =0.12 guded flter r=15, ε=0.12 2

21 Example flash/no-flash denosng gradent reversal nput p (no-flash) jont blateral flter σ s =8, σ r =0.02 jont blateral guded flter gude I (flash) guded flter r=8, ε=0.02 2

22 Beyond smoothng Applcatons: featherng/mattng, haze removal gude I very small ε preserve most gradents output q q ai nput p

23 Example featherng gude I (sze 3000x2000)

24 Example featherng flter nput p (bnary segmentaton)

25 Example featherng flter output q (alpha matte)

26 Example featherng gude I flter nput p flter output q 0.3s mage sze 6M mattng Laplacan [Levn et al. 06] 2 mn

27 Example haze removal gude I flter nput p (dar channel pror [He et al. 09]) flter output q

28 Example haze removal gude I guded flter (<0.1s, 600x400p) global optmzaton (10s)

29 Lmtaton What s an edge nherently ambguous, context-dependent weaer edge halo halo stronger texture Input Blateral flter σ s =16, σ r =0.4 Guded flter r=16, ε=0.4 2

30 Concluson We go from BF to GF Edge-preservng flterng Non-teratve O(1) tme, fast, accurate Gradent preservng More generc than smoothng Than you!