A New SURE Approach to Image Denoising : Interscale Orthonormal Wavelet Thresholding Florian Luisier, Thierry Blu, Senior Member, IEEE, and Michael Unser,

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Presentation transcript:

A New SURE Approach to Image Denoising : Interscale Orthonormal Wavelet Thresholding Florian Luisier, Thierry Blu, Senior Member, IEEE, and Michael Unser, Fellow, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 16, NO. 3, MARCH

Outline Introduction SURE Approach to Image Denoising PSNR Comparisons and Visual Quality Computation Time CONCLUSION 2

Introduction A nonredundant transform may match the performance of redundant ones. Do not make any explicit hypotheses on the clean image. Near-optimal performance—both regarding quality and CPU requirement 3

SURE Approach to Image Denoising(1/5) 4 Our goal is to find a function that minimizes By Stein’s Lemma and leads to

SURE Approach to Image Denoising(2/5) 5 The sensitivity of the soft-thresholding function with respect to the value of T is high.

SURE Approach to Image Denoising(3/5) 6 Build a linearly parameterized denoising function of the form This linear system is solved for a by

SURE Approach to Image Denoising(4/5) 7

SURE Approach to Image Denoising(5/5) 8 The number of terms K and the parameter T can be fixed independently of the image.

Interscale Predictor 9

10

PSNR Comparisons and Visual Quality 11

PSNR Comparisons and Visual Quality 12

PSNR Comparisons and Visual Quality 2 important criteria of judging visual quality are widely used ： – The visibility of processing artifacts can be reduced by taking into account intrascale dependencies – The conservation of image edges can be reduced by a careful consideration of interscale dependencies in the denoising function 13

Computation Time 14

CONCLUSION Demonstrate the efficiency of our SURE-based approach (best output PSNRs for most of the images). The visual quality of our denoised images is moreover characterized by fewer artifacts than the other methods. 15