Parallel Acceleration Algorithm for Wavelet Denoising of UAVAGS Data Based on CUDA

Abstract The computational efficiency is low when the vast volume of unmanned aerial vehicle airborne gamma-ray spectrum (UAVAGS) data is handled by wavelet denoising in CPU. So, a CUDA-based GPU parallel solution is recommended to resolve this issue in this paper. This proposed solution aims to significantly enhance the efficiency of parallel acceleration for wavelet denoising of UAVAGS data. In the preliminary stage, experiments were conducted with varying block sizes to investigate the influence of different block sizes on processing time. The objective was to identify the most suitable block size for efficiently processing UAVAGS data. Subsequently, a performance evaluation was conducted by comparing the acceleration ratios of GPU and CPU for different data volumes, as well as varying wavelet basis functions under the same data volume conditions. Finally, by intentionally introducing noise, calculations were performed to determine the optimal wavelet basis function concerning signal-to-noise ratio after denoising. The research findings indicate that the optimal two-dimensional block size falls within the range of 64×64 to 128×128. The majority of wavelet basis functions achieved acceleration ratios exceeding 100-fold in total processing time, with the coif5 wavelet basis function reaching an acceleration ratio of 185-fold. Comparative analysis of various denoising functions revealed that, under low signal-to-noise ratios, these functions exhibited insufficient denoising effects, while at high signal-to-noise ratios, there was a risk of excessive denoising. However, significant denoising effects were observed when employing hard thresholding with coif5, soft thresholding, and an improved thresholding method with db3.