Procedural Kernel (Neural) Networks (2022) (bartwronski.com)

A recent technical report explores the innovative use of tiny, shallow neural networks, termed Procedural Kernel Networks, to enhance traditional image processing techniques. While neural networks are increasingly dominant in image-related tasks, classic algorithms still underpin many essential applications due to their efficiency and lower computational demand. The research demonstrates that these miniature networks, with fewer than 20,000 parameters and trained in just 10 minutes, can significantly enhance the performance of established denoising methods like bilateral and non-local means filters without extensive manual tuning. This approach is particularly noteworthy for the AI/ML community as it offers interpretable results, which are critical for reliability in sensitive fields like scientific imaging and medicine. The study also evaluates various kernel functions, including anisotropic Gaussian blurs and polynomial reblurring, providing better outcomes than traditional methods while optimizing for peak signal-to-noise ratio (PSNR). The findings highlight a promising direction for combining classical image processing techniques with modern neural architectures, ultimately leading to more efficient and reliable image analysis solutions that could redefine practices in various industries without the extensive resource demands typically associated with larger neural networks.