A Novel Framework for Real ICMOS Image Denoising: LD-NGN Noise Modeling and a MAST-Net Denoising Network

Intensified complementary metal-oxide semiconductor (ICMOS) sensors involve multiple steps, including photoelectric conversion and photoelectric multiplication, each of which introduces noise that significantly impacts image quality. To address the issues of insufficient denoising performance and poor model generalization in ICMOS image denoising, this paper proposes a systematic solution. First, we established an experimental platform to collect real ICMOS images and introduced a novel noise generation network (LD-NGN) that accurately simulates the strong sparsity and spatial clustering of ICMOS noise, generating a multi-scene paired dataset. Additionally, we proposed a new noise evaluation metric, KL-Noise, which allows a more precise quantification of noise distribution. Based on this, we designed a denoising network specifically for ICMOS images, MAST-Net, and trained it using the multi-scene paired dataset generated by LD-NGN. By capturing multi-scale features of image pixels, MAST-Net effectively removes complex noise. The experimental results show that, compared to traditional methods and denoisers trained with other noise generators, our method outperforms both qualitatively and quantitatively. The denoised images achieve a peak signal-to-noise ratio (PSNR) of 35.38 dB and a structural similarity index (SSIM) of 0.93. This optimization provides support for tasks such as image preprocessing, target recognition, and feature extraction.