A parallel patch based algorithm for CT image denoising on the Cell Broadband Engine
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1 A parallel patch based algorithm for CT image denoising on the Cell Broadband Engine Dominik Bartuschat, Markus Stürmer, Harald Köstler and Ulrich Rüde Friedrich-Alexander Universität Erlangen-Nürnberg,Germany October 28, 2009 Dominik Bartuschat Chair for System Simulation Page 1/22
2 Contents 1 Motivation 2 Method 3 Cell implementation 4 Results Dominik Bartuschat Chair for System Simulation Page 2/22
3 CT image acquisition CT scanner acquires projection data Original object is reconstructed from them by filtered back projection Resulting image exhibits different kinds of (unknown) noise Figure: CT geometry Dominik Bartuschat Chair for System Simulation Page 3/22
4 CT image denoising Denoising helps to reduce high radiation dose for CT by increasing the signal to noise ratio Keep medical relevant information! Noise in CT images is instationary and distribution unknown (a) CT image (b) Noise Images provided by Anja Borsdorf, Siemens AG - Healthcare Sector Adaption to spatially changing noise behavior [BRH08] estimating noise distribution from two spatially identical input images with uncorrelated noise Dominik Bartuschat Chair for System Simulation Page 4/22
5 Denoising using K-SVD Decomposition of image into overlapping patches Sparse representation of patches linear combination of few noise - free dictionary atoms Dictionary is trained on noisy image itself without learning (gaussian) noise Dominik Bartuschat Chair for System Simulation Page 5/22
6 CT image denoising on Cell CT image denoising should be as fast as possible (realtime) Patches do not depend on each other trivially parallelizable Implementation on Cell Broadband Engine Architecture Heterogeneous multicore distributed memory processor Comprising PowerPC Processor Element (PPE) and Synergistic Processor Elements (SPEs) From projects.nsf/pages/multicore.cellbe.html Dominik Bartuschat Chair for System Simulation Page 6/22
7 1 Motivation 2 Method 3 Cell implementation 4 Results Dominik Bartuschat Chair for System Simulation Page 7/22
8 Theory: Sparse Representations Remember: Any vector x R n can be represented by a linear combination of n basis vectors that span a vector space Idea: Use more than n basis vectors (atoms) x can be represented by linear combination of only few atoms The set of prototype signal-atoms is an overcomplete dictionary D Find sparsest representation for x: â = argmin a 0 subject to Da x 2 2 ɛ a Dominik Bartuschat Chair for System Simulation Page 8/22
9 Sparse Coding with Batch OMP Solve overdetermined linear system while finding the sparsest solution in general NP-hard Efficient Orthogonal Matching Pursuit (OMP): Greedy algorithm, selects atoms sequentially Select atom with highest correlation to the current residual r ˆk = argmax dk T r, I = k I ˆk Orthogonalization: Project signal orthogonally to span of selected atoms and recompute residual r = x D I D + I x Dominik Bartuschat Chair for System Simulation Page 9/22
10 Sparse Coding with Batch OMP Solve overdetermined linear system while finding the sparsest solution in general NP-hard Efficient Orthogonal Matching Pursuit (OMP): Greedy algorithm, selects atoms sequentially Select atom with highest correlation to the current residual r ˆk = argmax dk T r, I = k I ˆk Orthogonalization: Project signal orthogonally to span of selected atoms and recompute residual r = x D I D + I x More efficient Batch OMP (R. Rubinstein, M. Elad): no need to compute r, only product with D T (precompute G = D T D) p = D T r = p 0 G I (D T I D I ) 1 D T I x Dominik Bartuschat Chair for System Simulation Page 9/22
11 Sparse Coding with Batch OMP Solve overdetermined linear system while finding the sparsest solution in general NP-hard Efficient Orthogonal Matching Pursuit (OMP): Greedy algorithm, selects atoms sequentially Select atom with highest correlation to the current residual r ˆk = argmax dk T r, I = k I ˆk Orthogonalization: Project signal orthogonally to span of selected atoms and recompute residual r = x D I D + I x More efficient Batch OMP (R. Rubinstein, M. Elad): no need to compute r, only product with D T (precompute G = D T D) p = D T r = p 0 G I (D T I D I ) 1 D T I x Instead of full Pseudoinverse computation computation of Progressive Cholesky Update Dominik Bartuschat Chair for System Simulation Page 9/22
12 K SVD algorithm for image denoising Computation of the denoised image ˆX from the noisy image Y and training of the dictionary D [EA06]: ˆX = argmin{λ Y X X,D,A ij µ ij a ij 0 + ij Da ij R ij X 2 2} Figure: K SVD algorithm overview, adapted from presentation of M.Elad Dominik Bartuschat Chair for System Simulation Page 10/22
13 Extension for CT-Image Denoising Computation of the denoised image ˆX from the noisy image Y and training of the dictionary D [EA06]: ˆX = argmin{λ Y X ij µ ij a ij 0 + X,D,A ij Da ij R ij X 2 2} Estimated local noise variance V as error tolerance for Batch OMP: ij min a ij a ij 0 s.th. Da ij R ij Y C V (R ij Y ) with different C for dictionary training (C Train ) and image denoising (C Den ) Weight λ ij for denoised and noisy average image, dependent on Variance λ (V (R ij Y )): ˆX = min { X ij λ ij R ij Y R ij X ij Da ij R ij Y 2 2} Dominik Bartuschat Chair for System Simulation Page 11/22
14 1 Motivation 2 Method 3 Cell implementation 4 Results Dominik Bartuschat Chair for System Simulation Page 12/22
15 Cell - Parallelization and Performance For high performance, the following kinds of parallelism must be exploited Thread-level parallelism: independent SPE threads can work concurrently on different data and tasks Data-level parallelism: vector processing with SIMD, controllable by intrinsics Parallelization of data transfer and computations: Programmer has to explicitly transfer data and instructions to local storage of SPEs can use buffering to overlap transfers with computations Dominik Bartuschat Chair for System Simulation Page 13/22
16 Thread Parallelism Unknown a priori, how long SPE thread needs to denoise patches Dynamically distribute work among SPE threads Synchronize threads by means of an atomic counter Assign one stripe of noisy image to each SPE thread Having denoised stripe, SPE increments atomic counter and denoises next stripe Each SPE has own space in main memory to which denoised patches are transferred no synchronization for writing Dominik Bartuschat Chair for System Simulation Page 14/22
17 Data Transfer and Cache Blocking Transfer image data blocks from stripe to buffer in local storage patches to be transferred to local storage have to be aligned in memory SPE extracts patches from buffers (byte shuffling, SIMD), performs Batch OMP and reconstructs patches Two buffers are needed at once to extract overlapping patches Multibuffering is used to overlap computations and data transfer Dominik Bartuschat Chair for System Simulation Page 15/22
18 Optimizations on SPEs Difficulties for Batch OMP algorithm on Cell: Cholesky matrix increases by one row in each iteration SIMD vectorization not trivial Only subset of atoms is chosen Gather-operations in vectors and matrices Only small but increasing amount of coefficients loops with short bodies and few iterations are inefficient, due to in-order execution and high branch miss penalty Dominik Bartuschat Chair for System Simulation Page 16/22
19 Optimizations on SPEs Difficulties for Batch OMP algorithm on Cell: Cholesky matrix increases by one row in each iteration SIMD vectorization not trivial Only subset of atoms is chosen Gather-operations in vectors and matrices Only small but increasing amount of coefficients loops with short bodies and few iterations are inefficient, due to in-order execution and high branch miss penalty Applied techniques to increase performance: Dictionary size restricted to multiples of 8 and fixed at compile time, together with maximum number of coefficients simplifies address calculation SIMD vectorization and unrolling of loops Register blocking to reduce load and store operations Dominik Bartuschat Chair for System Simulation Page 16/22
20 1 Motivation 2 Method 3 Cell implementation 4 Results Dominik Bartuschat Chair for System Simulation Page 17/22
21 Performance Comparisons Performance for denoising a 512 x 512 image for patches of size 8 x 8 with C Den = 20 (mostly 4 atoms) Denoising of patches on QS22, superposition and computations of weights for each SPE separately. SPEs Runtime [ms] Parallel efficiency [%] Denoising of patches on different architectures (computation of one final superposition and weights image). Cores / SPEs Runtime [s] on QS Runtime [s] on Nehalem Runtime [s] on Penryn QS22: BladeCenter QS22, contains two PowerXCell 8i 3.2 GHz Nehalem: Intel Core i7 CPU 2.93GHz Penryn: Intel Core 2 Quad CPU 2.83GHz Dominik Bartuschat Chair for System Simulation Page 18/22
22 Denoising by K-SVD (a) K-SVD 2D original (b) K-SVD 2D denoised Image provided by Anja Borsdorf, Siemens AG - Healthcare Sector Dominik Bartuschat Chair for System Simulation Page 19/22
23 Denoising by K SVD (a) Original, provided by A. Borsdorf, Siemens AG - Healthcare Sector (b) K-SVD 2D (c) K-SVD 2D Original Dominik Bartuschat Chair for System Simulation Page 20/22
24 Conclusion We optimized Batch-OMP algorithm on Cell BE and applied it to CT image denoising Denoising algorithm preserves edges and adapts to spatially varying noise Parallel efficiency of SPE computations more than 97% on Playstation3 and 87% on QS22 Optimized version of denoising on Cell more than 6 times higher per-core performance than not hand-optimized (but well-written) OpenMP implementation on latest multicore architectures Sequential computations on PPE cause performance stagnation (and even increasing runtime) Dominik Bartuschat Chair for System Simulation Page 21/22
25 Related Literature [BRH08] A. Borsdorf, R. Raupach, and J. Hornegger. Multiple CT-reconstructions for locally adaptive anisotropic wavelet denoising. International Journal of Computer Assisted Radiology and Surgery, 2(5): , [EA06] [Gsc07] [RZE08] M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process, 15(12): , M. Gschwind. The Cell Broadband Engine: Exploiting Multiple Levels of Parallelism in a Chip Multiprocessor. International Journal of Parallel Programming, 35(3): , R. Rubinstein, M. Zibulevsky, and M. Elad. Efficient Implementation of the K-SVD Algorithm and the Batch-OMP Method. ronrubin, Technical Report CS Technion, Dominik Bartuschat Chair for System Simulation Page 22/22
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