In acoustic resolution PA microscopy (AR-PAM), high signal-to-noise ratio (SNR) images require averaging pulse-illuminated A-scans, slowing data acquisition. Multi-wavelength PA imaging further reduces scan speed, leading to issues like photobleaching of exogenous contrast agents due to prolonged exposure. Traditional noise removal algorithms fall short, while deep learning models like U-Net, though effective, also compromise image contrast. We propose a platform-flexible denoising conditional GAN (modified Pix2Pix) to generate high SNR images with single pulse illumination, reducing AR-PAM scan time by 30-fold. Tested across various systems, our model minimizes photobleaching, enhances efficiency, and remains adaptable to diverse hardware setups without retraining, broadening its preclinical and clinical applications.