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Wavelet-Domain Video Denoising Based on Reliability Measures

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Wavelet-Domain Video Denoising Based on Reliability Measures. Vladimir Zlokolica , Aleksandra Piˇ zurica and Wilfried Philips Circuits and Systems for Video Technology 2006 , Transaction on IEEE Journals. Outline. Introduction Proposed Video Denoising Method Noise Estimation[36] - PowerPoint PPT Presentation

Text of Wavelet-Domain Video Denoising Based on Reliability Measures

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Wavelet-Domain Video Denoising Based on Reliability MeasuresVladimir Zlokolica, Aleksandra Pizurica and Wilfried PhilipsCircuits and Systems for Video Technology2006, Transaction on IEEE JournalsOutlineIntroductionProposed Video Denoising MethodNoise Estimation[36]Reliability of MV EstimatesMotion EstimationRecursive Temporal Filtering (RTF)Adaptive Spatial FilteringExperimental ResultsConclusions

Video denoising: spatial-temporal filters.Filtering:Nonseparable (fully 3-D) [2][6]Separable (2-D +1-D) [7][14]Combined: weighting for spatial and temporal?Spatial-first: ringing or blurring at high noise levels.Temporal-firstWe adopt the temporal-first approach, and develop a robust motion estimation method.IntroductionProposed Video Denoising Method

Noise EstimationAssume that most frequent gradient amplitude is predominately caused by noise:

It can be related to the input noise level:

Finally, we recursively update estimated s :

[36]V. Zlokolica, A. Pizurica, and W. Philips, Wavelet domain noise-robust motion estimation and noise estimation for video denoising,presented at the 1st Int. Workshop Video Process. Quality Metrics Consum. Electron., Scotssdale, AZ, Jan. 2005, Paper no. 200.

Parameters k1 = 0.001, k2 = 1.069 and k3 =2.213. value is almost independent of image context and strongly corresponds to noise level.

Noise Estimation

[36]V. Zlokolica, A. Pizurica, and W. Philips, Wavelet domain noise-robust motion estimation and noise estimation for video denoising,presented at the 1st Int. Workshop Video Process. Quality Metrics Consum. Electron., Scotssdale, AZ, Jan. 2005, Paper no. 200.Proposed Video Denoising Method

Proposed Video Denoising MethodThe proposed method uses a nondecimated wavelet transform[35].Denote wavelet bands: WB={LL,HL,LH,HH}The spatial position as: r = (x,y)The decomposition level: l = 1,,N (1 denotes the finest scale and N the coarsest)WBn: noisy band; WBtf: temporally filtered; WBstf: spatio-temporally filtered band.Reliability of MV EstimatesWe define the MAD for each block s in the wavelet band WB(l)(r,t), as follows:

Bs : the set of r belonging to the given 8x8 block.WB: {LL,HL,LH,HH}N: the maximum decomposition level.

Reliability of MV Estimates

Motion EstimationWavelet-Domain Three-Step Method:Estimates first MV field at the roughest scale and in the following steps refines the MV field:

vpi {0,s,s,t,t}; P(0) = 0, P(vpi) = 2.5

v(1)cx , v(1)cy {-8,-4,0,4,8}; v(2)cx , v(2)cy {-4,-2,0,2,4};v(3)cx , v(3)cy {-2,-1,0,1,2};

Motion EstimationThe cost function:

Where the constants C1 and C2 are optimized to obtain a noise robust and smooth MV field: C1= 1, C2 = 1.45Assign more weight to the cost function for higher H and V for the tested nonzero correction.

Proposed Video Denoising Method

Recursive Temporal Filtering (RTF)Wavelet domain temporal filtering:

When WB(l)tf(r-vb,t-1) has not all been filtered, noisy wavelet coefficient will propagate.

Recursive Temporal Filtering (RTF)To solve this problem, we update (l)WB(s,t,n,vb) with a correction function:

When (l)WB(r-vb,t-1) 0, (l) WB*(r,t) 0.5: Both frames are noisy, perform simple averaging.When (l)WB(r-vb,t-1) 1, (l) WB*(r,t) (l) WB(r,t).Furthermore, we apply = 1 at least two time-recursions with reliable MVs have been applied in the last two frames.

Proposed Video Denoising Method

Adaptive Spatial FilteringLet (rc) denote the neighborhood surrounding the central pixel rc :

Where T = MAD(l)WB(s,t,vb) , km = 1.The lower MAD the for the corresponding wavelet band WB(l) and block s, the less we will average.

Experimental Results

Fig. 4. Results for the 29th frame of Bicycle sequence with added Gaussian noise(n= 15), processed by (c) WRTF filter and (d) 3RDS filter [16].

(a) Original image frame. (b) Noisy image frame.(c) WRTF(d) 3RDS filter [16]Experimental ResultsFig. 7. Results for the 75th frame of the processed Flower Garden sequence with added Gaussian noise (n = 15), by (c) the 3DWTF algorithm, and (d) theWRSTF algorithm.

(a) Original image frame. (b) Noisy image frame.(c) 3DWTF(d) WRSTF

Experimental Results

(a)(c)(b)(d)

Experimental ResultsExperimental Results

ConclusionsWe have proposed a new method for motion estimation and image sequence denoising in the wavelet domain.By robustly estimating motion and compensating, we efficiently remove noise without introducing visual artifacts.In future work, we intend to refine our motion estimation framework in order to deal with occlusion and moving block edges.

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