3D GNLM: Efficient 3D Non-Local Means Kernel with Nested Reuse Strategies for Embedded GPUs

The 3D Non-Local Means (NLM) algorithm has become a crucial preprocessing technique for 3D image datasets due to its effectiveness in denoising while preserving fine details. This method has been proven to be highly efficient in high-demand tasks within industrial applications such as medical imaging and remote sensing. The 3D NLM algorithm computes the filtered value for each voxel by calculating the weighted average of all voxels within a 3D search window, where the weights are determined by the similarity between pairs of 3D template windows. Therefore, the computational burden becomes significant, especially in embedded GPUs with limited computational power and memory resources. To address this issue, we propose an efficient GPU parallel kernel to minimize redundant computations and memory accesses. The kernel integrates three nested reuse strategies to handle redundant computations in three dimensions: for columns, we leverage the fast data exchange mechanism to reuse column computation results via on-chip registers; for rows, we use a sliding window strategy, utilizing GPU global memory as an intermediary to store and reuse similarity values between filtered rows; and for channels, we introduce a zigzag scanning strategy that enables simultaneous computation across multiple channels and employs on-chip registers to facilitate channel computation reuse. Experimental results demonstrate that our kernel achieves an average speedup of 7.7x on the embedded Jetson AGX Xavier platform across a range of 3D image datasets compared to existing methods, showcasing exceptional performance.