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Chirag Jain, Sriram Sethuraman Ittiam Systems (Pvt.) Ltd., Bangalore, India

A LOW-COMPLEXITY, MOTION-ROBUST, SPATIO-TEMPORALLY ADAPTIVE VIDEO DE-NOISER WITH IN-LOOP NOISE ESTIMATION. Chirag Jain, Sriram Sethuraman Ittiam Systems (Pvt.) Ltd., Bangalore, India Image Processing, 2008. ICIP. Outline. Introduction Proposed Algorithm LLMMSE based spatial filtering

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Chirag Jain, Sriram Sethuraman Ittiam Systems (Pvt.) Ltd., Bangalore, India

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  1. A LOW-COMPLEXITY, MOTION-ROBUST, SPATIO-TEMPORALLY ADAPTIVE VIDEODE-NOISER WITH IN-LOOP NOISE ESTIMATION Chirag Jain, SriramSethuraman Ittiam Systems (Pvt.) Ltd., Bangalore, India Image Processing, 2008. ICIP

  2. Outline • Introduction • Proposed Algorithm • LLMMSE based spatial filtering • Temporal filtering • Noise estimation • Experimental Results • Complexity Comparison • ConclusionsandFutureWork

  3. Introduction • Many of the successful techniques determine the weights needed to combinethe neighborhood of the pixelbeing de-noised. • e.g., bi-lateral[2], anisotropic diffusions method[3], spatial non local means (NL-means) de-noiser [4]. • Drawback: high computational complexity [2] C. Tomasi, and R. Manduchi.,” Bilateral filtering for gray and color images,” Proceedings of the Sixth International Conference on Computer Vision, pp. 839-846, 1998. [3] P. Bourdon, B. Augereau, C. Olivier, and C. Chatellier, “Noise removal on color image sequences using coupled anisotropic diffusions and noise-robust motion detection,” EUSIPCO'04 -Signal Processing XII, Wien (Austria), pp. 1194 – Sep. 2004. [4] A. Buades, B. Coll, and J. M. Morel, “Denoisingimage sequences does not require motion estimation,” IEEE Int. Conf. on Advanced Video and Signal based Surveillance, 2005.

  4. Introduction • We propose a de-noising algorithm using the following principles: • Block-based noise estimation and de-noising; • Use of block motion vectors by a video encoder; • Locally adaptive Linear Minimum Mean Square Error (LLMMSE) methods. • Reliance on Infinite Impulse Response (IIR) temporal filtering. • Spatially adaptive weight selection for the spatial-temporal IIR (ST-IIR) filter.

  5. Proposed Algorithm Case 1: stationary blocks Case 2: under-compensated blocks Case 3: well Motion Compensated blocks

  6. LLMMSE based spatial filtering • An 3x3 Gaussian filter is applied to obtain a output L (low pass filtered ). • The output H = X - L (X is the noisy image). • Spatially filtered output X’(i,j) is computed as: • σf2: variance of noisy signal(X) for every 8x8 block. • σn2: the estimated noise variance of the sequence.

  7. Temporal filtering • Let X(n) be the current frame,Y(n-1) be the previous denoised frame. • σfr_diff2 : the estimated varianceofthedifference data instationary areas between X(n-1) and Y(n-2) • σblk_diff2 : the varianceof the difference between cur_blk and its co- located block in Y(n-1) (prev_blk)

  8. Temporal filtering • Is block stationary? If (2) is satisfied and at least one of (3) or (4) are true: • Case 1: stationary blocks(CL) • Is MC successful? If (5) is satisfied: • Case 3: well Motion Compensated blocks(MC) • All remaining blocks belong to case 2: • Case 2: under-compensated blocks

  9. Temporal filtering • Case 2 : • Blocks only spatially filtered • Case 1&3 : • The ST-IIR filtering is performed at a pixel level by using the CL or MC pixel in Y(n-1): • pixel_diff: the difference between the current pixel and its CL orMC pixel in Y(n-1). • For pixels with pixel_diff > σfr_diff(n-1), α=e^(-1/k4) σfr_diff(n-1) :the deviationofstationary (n-1)

  10. Noise estimation • If the current frame X(n) has noise variance σn2and Y(n-1) has noise variance σp2, the variance σfr_diff2(n) (stationary)will be: • Ideally, σp2 should be zero, but we should introduce a confidence c for robustness: • c: the average value of αused for ST-IIR.

  11. Noise estimation • If the Y(n-1)is not filtered, c = 1, σn2= σfr_diff2 . • The convergence of standard deviation of thenoise against frame number for two sequences:

  12. Experimental Results • Additive white Gaussian noise of a specified variancewasadded to clean video sequences. • Encodedusing a H.264 baseline profile encoder. • NL-means[4]: window size of 21x21 and a similarity square neighborhoodof 7x7 with (a = 1 and h = n) [4].

  13. Experimental Results

  14. Experimental Results

  15. Complexity Comparison • The proposed algorithm performthe decisionsat a block level and the filteringat a pixel level. • The proposed algorithm requires only 38operations(+,-,*,look-ups) per pixel • NL-means has a complexity of 441x49 weighted Euclideandistance calculations and exponential function lookups per pixel.

  16. ConclusionsandFutureWork • Theproposed algorithmcomesclose to the performance of more complex algorithms such as theNL-means method. • Itis ideally suited for real timeembedded implementations. • The spatial filter’s output can be used by the encoder to avoid spurious motion vectors.

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