Zero-shot image denoising with hollow pair sampling and noise-aware attention

Image acquisition, compression, or transmission processes frequently introduce noise distortions, which can significantly degrade the visual quality of images. Existing zero-shot denoising methods face challenges in effectively differentiate between noise and subtle image content variations, especially when dealing with structured noise, non-zero-mean noise, and high-noise environments. As a result, these methods may lead to over-smoothing or the loss of fine details, ultimately degrading the overall image quality. In this paper, we propose a novel image denoising technique that does not rely on clean reference images for training. Instead, we utilize secondary sampling and convolution from the original noisy images to generate denoised images with enhanced contrast. Our sampling strategy expands upon the Zero Noise2Noise (ZS-N2N) approach, eliminating the need for additional noise models or parameters. By employing a straightforward hollow filter and noise-aware attention, our method achieves high-quality denoising across various noise types and levels while effectively distinguishing meaningful image features from noisy patterns. Experimental evaluations on visible light and infrared images demonstrate the effectiveness of our approach. Notably, our method excels in restoring image details. Under Gaussian noise, the visible image achieves the PSNR of 37.78 and an SSIM of 0.9460, while the infrared image attains the PSNR of 36.61 and an SSIM of 0.9415. Overall, our method successfully mitigates noise distortion while preserving rich image details, significantly enhancing visual quality.