(i) {Î± - Convex Optimization

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CS reconstruction (2) Projections Real Fourier SNR=35.2649 dB Projections Real Fourier SNR=35.2649 dB Original phantom image Projections Real Fourier SNR=35.2649 dB Original phantom image TV−DR on on noiseless data Iter=200 PSNR=75.4585 dB dB TVDN−DR on on noisy data Iter=200 PSNR=40.7432 dB dB TV−DR on noiseless data Iter=200 PSNR=75.4585 dB TVDN−DR on noisy data Iter=200 PSNR=40.7432 dB Stanford seminar 08-26
Inpainting and CS H = Dirac, Φ = Curvelets + LDCT, Ψ(α) =‖α‖ l1 , m/n = 20% Stanford seminar 08-27
Page 1 and 2: Optimization problems in compressed
Page 3 and 4: Compressed/ive Sensing Stanford sem
Page 5 and 6: Compressed/ive Sensing Common wisdo
Page 7 and 8: Compressed/ive Sensing Common wisdo
Page 9 and 10: Compressed/ive Sensing (cont’d) C
Page 11 and 12: Compressed/ive Sensing (cont’d) C
Page 13 and 14: Convex analysis and operator splitt
Page 15 and 16: Class of problems in CS (cont’d)
Page 17 and 18: Class of problems in CS (cont’d)
Page 19 and 20: Characterization Theorem 1 (i) Exis
Page 21 and 22: Operator splitting schemes Idea: re
Page 23 and 24: Proximity operators Some properties
Page 25 and 26: Example of proximity operator Stanf
Page 27 and 28: Compressed sensing optimization pro
Page 29 and 30: Characterizing Problem (P τ ) Stan
Page 31 and 32: Proximity operators of Ψ Conclusio
Page 33 and 34: DR to solve Problem (P σ ) Theorem
Page 35 and 36: DR to solve Problem (Peq) Theorem 1
Page 37 and 38: Pros and cons (P σ ) and (P eq ) h
Page 39: CS reconstruction (1) H = Fourier,
Page 43 and 44: Inpainting and CS H = Dirac, Φ = C
Page 45 and 46: Computation time CS H = Fourier, Φ
Page 47 and 48: Ongoing and future work Beyond the

(i) {Î± - Convex Optimization

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