A GPU-Accelerated Modified Unsharp-Masking Method for High-Frequency Background- Noise Suppression

Abstract

A digitized analog signal often encounters a high-frequency noisy background which degrades the signal-to-noise ratio (SNR) particularly in case of low signal strength. Despite quite a lot of hardware-and software-based approaches have been reported to date to deal with the noise issue, it is still a challenging task to real-time retrieve the noise-contaminated low-frequency information efficiently without degrading the original bandwidth. In this paper, we report a modified unsharp-masking (UM)-based Graphics Processing Unit (GPU)-accelerated algorithm to efficiently suppress a high-frequency noisy background in a digitized two-dimensional image. The proposed idea works effectively even if noisedensity is high and signal of interest is comparable or weaker than the maximum noise level. While suppressing the noisy background, the original resolution remains least compromised. We first explore the effectiveness of the algorithm by means of simulated images and subsequently extend our demonstration towards a real-world life-science imaging application. Securing a potential for real-time applicability, we implement the algorithm via Compute Unified Device Architecture (CUDA)-acceleration and preserve a <300 µs processing time for a 1000×1000-sized 8-bit data set.

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

“…Qianjiao Wu et al [29] CUDA algorithm improved the computational efficiency of multiscale DEM analysis, reducing response times.Engels et al [30]'s CUDA-SHAPE algorithm and Axel Davy et al [31] GPU-accelerated denoising solution both enhanced the performance of their respective software and algorithms. The works of Bhaskar Jyoti Borah et al [32] and Dariusz Puchala and Kamil Stokfiszewski [33] further demonstrated the effectiveness of GPU acceleration in image processing.Tianru Xue et al [34] real-time anomaly detection technology and Raghav G. Jha et al [35] TRG accelerated computation method, along with Qiyang Xiong et al [36] PIC simulation optimization scheme, all showcase the potential of GPUs in enhancing remote sensing data processing capabilities. These studies provide new directions for efficient processing of remote sensing data.…”

Unsharp masking image enhancement the parallel algorithm based on cross-platform

“…Zhu 20 proposed an improved self-adaptive UM image enhancement algorithm, which improves the operation speed of the algorithm. Borah et al 21 proposed an improved GPU-based UM acceleration algorithm, which effectively suppresses the high-frequency noise background in the image and improves the execution speed of the algorithm.…”