Fast single-image denoising through self-observation

Self-supervised neural networks have demonstrated remarkable performance in image-denoising applications. However, existing dataset-free methods have limitations, including their high computational overhead, noise model requirements, and compromised image quality. To address these problems, we proposed a dataset-free method for image denoising using a single noisy image. The proposed method uses a lightweight neural network with slightly more than 3000 parameters to achieve effective denoising. Inspired by the Zero-Shot Noise2Noise framework, we downsampled noisy images and employed downscaled nonfused images for training, achieving denoising by cross-mapping the denoised subimages with the original noisy subimages. To further reduce the high-frequency noise, we employed cross-mapping among the denoised subimages. The numerical results demonstrated the superior performance of the proposed algorithm compared with other dataset-free neural network algorithms. The proposed method exhibited a shorter processing time and fewer network parameters yet yielded denoised images with a higher signal-to-noise ratio. Moreover, it demonstrated superior performance and processing speed in both synthetic and actual noise experiments, making it suitable for practical applications.