Two-Dimensional Variational Mode Decomposition for Noise Suppression in Nanoscale Optoelectronic Signal Processing

This paper proposes a denoising method based on two-dimensional variational mode decomposition (2D-VMD) and a subtraction-average-based optimizer (SABO) for enhancing signal clarity in nanoscale optoelectronic systems. The SABO algorithm optimizes the decomposition parameters (mode number K and penalty factor α ) using minimum image entropy, enabling efficient separation of noise from high-resolution signals in nanophotonic imaging or quantum dot-based sensors. The 2D-VMD decomposes multi-channel optoelectronic data into intrinsic mode functions (IMFs), which are analyzed via frequency-wavenumber spectra and cross-correlation to distinguish noise from coherent signals. Numerical experiments demonstrate that SABO converges faster than particle swarm optimization (PSO), and the 2D-VMD-SABO method achieves superior noise suppression while preserving critical signal features. Applied to simulated terahertz optoelectronic data, the method improves the peak signal-to-noise ratio (PSNR) by 24%, highlighting its potential for nanoelectronic device characterization and optical metrology.