The visualization of grain components through non-destructive detection is crucial for crop improvement and quality assessment, making it a research focus. Raman chemical imaging, an effective spectral imaging technology, has been extensively applied to detect various grain components. However, practical applications face challenges such as unclear Raman shift-composition relationships, low-quality images affected by noise, and long imaging times. To address these issues, this paper proposes a novel method, Raman Denoising Diffusion Generative Adversarial Network (RDDGAN), based on the Denoisi...
