ZS-ACL: Light-Weight Zero-Shot Image Denoising Using Alpha-Conditional Loss

Zero-shot image denoising, the process of removing noise from images without ground truth, is becoming increasingly important across various fields. Current denoising methods often downsample noisy images and employ residual and consistency loss functions to learn and subtract noise from base image. However, these methods struggle to discern the superiority between different downsampled images. To address this limitation, we propose an alpha-conditional loss function, combined with a 3×3 window downsampler, and a light-weight convolutional neural network, which effectively handles various noise types and levels. Notably, our method, named ZS-ACL, is computationally efficient by consisting of just 6K model parameters, thus distinguishing itself from others in the field. The experimental results on established real-world datasets demonstrate that ZS-ACL either outperforms or matches existing approaches in various scenarios, even with significantly fewer parameters. It learns from only a single image, thus presenting an efficient dataset-free denoising solution. Moreover, it showcases versatility and robustness by achieving better results for varying noise levels. The code is made available on Github at https://github.com/kshahmir49/ZS-ACL.