R-LayerNorm: Robust Layer Normalization with Adaptive Noise Suppression for Noisy Image Data

We introduce R-LayerNorm, a novel normalization layer designed to handle noisy and corrupted image data by dynamically adjusting normalization strength based on local noise estimates. Unlike standard normalization methods that apply uniform scaling regardless of local corruption, R-LayerNorm incorporates a learn- able noise-sensitivity parameter (λ) and a spatial entropy-based noise estimator. When evaluated on the CIFAR-10-C benchmark across six diverse corruption types, R-LayerNorm achieves a statistically significant average accuracy improvement of +4.95% (p < 0.001) over standard BatchNorm, with particularly strong gains on contrast (+14.52%) and frost (+6.88%) corruptions. The method serves as a drop-in replacement for existing normalization layers, requires minimal computational over-head (∼10%), and demonstrates robust performance across multiple random seeds. Code is available at: https://github.com/R-LayerNorm/R-LayerNorm/tree/main.