A denoising model for MC rendered images based on fusion kernel prediction and generation of adversarial networks
yuqi yang · 2024
This research offers a denoising model that organically blends Generative Adversarial Network (GAN) and Kernel Prediction Network (KPN) to address the issue of probable loss of fine details in denoised Monte Carlo pictures. To preserve image details during the denoising process and overcome the denoising network's inadequacy in image feature extraction, the model introduces a Multi-Scale Feature Module to expand the receptive field of feature extraction, allowing the model to extract image features at different scales. And employs Haar Wavelet Transform to suppress high-frequency noise in images, hence increasing the model's denoising performance. The suggested network model efficiently lowers noise in diverse image scenes, improving quality metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).