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Slides12 - Course 18.327 and 1.130 Course Wavelets and...

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Unformatted text preview: Course 18.327 and 1.130 Course Wavelets and Filter Banks Mallat pyramid algorithm Pyramid Algorithm for Computing Pyramid Wavelet Coefficients Wavelet Goal: Given the series expansion for a function fj(t) in Vj Goal: (t) fj(t) = ∑ aj[k]φj,k(t) (t) k how do we find the series fj-1(t) = ∑ aj-1[k]φj-1,k(t) (t) k in Vj-1 and the series in gj-1(t) = ∑ bj-1[k]wj-1,k(t) (t) k in Wj-1 such that such in fj(t) = fj-1(t) + gj-1(t) ? (t) (t) (t) 2 Example: suppose that φ(t) = box on [0,1]. Then Example: (t) functions in V1 can be written either as a combination of can functions L φ(2t) 1 0 1 , ½ φ(2t-1) 0 L , ½1 or as a combination of L φ(t) 1 0 φ(t-1) 1 L 1 , 0 1 2 , 3 plus a combination of w(t) 1 w(t-1) 1 L L 0 1 , Easy to see because Easy 0 φ(2t) (2t) φ(2t –1) (2t 1) = = 1 ½[φ(t) ½[φ(t) (t) + - 2 , w(t)] w(t)] 4 • Suppose that f(t) is a function in L2(R). What are the (R). coefficients, aj[k], of the projection of f(t) on to Vj? [k], Call the projection fj(t), Call fj(t) = ∑ aj[k]φj,k(t) (t) k aj[k] must minimize the distance between f(t) and fj(t) [k] ∞ ∂ ∫ ∂aj[k] -∞ {f(t) – fj(t)}2 dt = 0 dt {f(t) ∞ ∫ 2 {f(t) - ∑aj[l]φj,l(t)} φj,k(t)dt = 0 (t)} dt {f(t) -∞ l f(t) aj[k] = ∫f(t)φj,k(t)dt [k] dt fj(t) 5 • How does φj,k(t) relate to φj-1,k(t), wj-1,k(t)? How (t) (t), N φ(t) = 2 ∑ h0[l]φ(2t - l) (2t (t) l=0 refinement equation φj-1,k(t) = 2(j-1)/2 φ(2j-1t-k) N = 2(j-1)/2. 2 ∑ h0[l]φ (2jt – 2k- l) l=0 N φj-1,k(t) = √2 ∑h0[l] φjj,2k + l(t) (t) ,2k l=0 Similarly, using the wavelet equation, we have N wj-1,k(t) = √2 ∑h1[l]φjj,2k + l(t) (t) ,2k l=0 6 Multiresolution decomposition equations Multiresolution decomposition ∞ a j-1[n] = ∫ f(t)φj-1,n(t) dt [n] (t) dt -∞ ∞ = √2 ∑ h0[l] ∫ f(t)φjj,2n + l(t) dt (t) dt ,2n l -∞ = √2 ∑ h0[l] aj[2n + l] [2n l So aj-1[n] = √2 ∑ h0[k-2n]aj[k] [n] k → Convolution with h0[-n] followed by downsampling n] downsampling 7 Similarly ∞ bj-1[n] = ∫ f(t) wj-1,n(t) dt [n] f(t) (t) dt -∞ which leads to bj-1[n] = √2 ∑ h1[k – 2n] aj[k] [n] [k 2n] k 8 Multiresolution reconstruction equation Multiresolution reconstruction Start with fj(t) = fj-1(t) + gj-1(t) (t) (t) Multiply by φj,n(t) and integrate Multiply ∞ ∞ ∞ -∞ -∞ -∞ ∫ fj(t) φj,n(t) dt = ∫ fj-1(t)φj,n(t)dt + ∫ gj-1(t)φj,n(t) dt (t) (t) dt (t) dt So ∞ aj[n] = ∑ aj-1[k] ∫ φj-1,k(t) φj,n(t) dt + [n] [k] (t) (t) k -∞ ∞ ∑ bj-1[k] ∫ wj-1,k(t) φj,n(t) dt [k] (t) (t) dt k -∞ 9 ∞ ∞ ∫ φj-1,k(t) φj,n(t) dt = √2 ∑ h0[l] ∫ φj,2k+l(t) φj,n(t) dt (t) (t) (t) dt -∞ l -∞ = √2 ∑ h0[l] δ[2k + l - n] [2k l = √2 h0[n – 2k] [n 2k] Similarly ∞ ∫ wj-1,k(t)φj,n(t) dt = √2 h1[n –2k] (t) [n -∞ Result: aj[n] = √2 ∑ aj-1[k]h0[n - 2k] + [n] [n k √2 ∑ bj-1[k]h1[n – 2k] [n k 10 10 Filter Bank Representation aj[n] u0[n] aj-1[n] v [n] ~ ↑2 0 √2h [n] ↓2 √2h0[n] 0 Synthesis Analysis v1[n] bj-1[n] u1[n] ~ ↑2 ↓2 √2h1[n] √2h1[n] ~ h0[n] = h0[-n] ~ h1[n] = h1[-n] ⊕ aj[n] time reversal Verify that filter bank implements MRA equations: ~ u0[n] = √2 ∑ h0[n - k]aj[k] [n] [n k = √2 ∑ h0[k – n]aj[k] [k k 11 11 aj-1[n] = u0[2n] [2n] downsample by 2 by = √2 ∑ h0[k – 2n]aj[k] [k k bj-1[n] = u1[2n] = √2 ∑ h1[k – 2n]aj[k] [k k 678 aj[n] = √2 ∑ h0[n - l]v0[l] + √2 ∑ h1[n - l]v1[l] [n] [n [n l l aj-1[0] aj-1[1] v0[n] L aj-1[l/2] ; l even /2] L v0[l] = 0 ; otherwise n -1 0 1 2 upsample by 2 So aj[n] = √2 ∑ h0[n - l]aj-1[l/2] + √2 ∑ h1[n - l]bj-1[l/2] [n] [n /2] [n l even = √2 ∑ h0[n –2k]aj-1[k] + √2 ∑ h1[n – 2k]bj-1[k] [n [k] [n k k 12 12 ...
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This note was uploaded on 12/04/2011 for the course ESD 18.327 taught by Professor Gilbertstrang during the Spring '03 term at MIT.

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