Spectral Scaling-Based Augmentation for Corruption-Robust Image Classification

Abstract

Image classifiers often degrade in performance when test images differ significantly from the training distribution due to real-world image corruptions. Frequency-based augmentations can be used to address this issue, but existing methods excel against corruptions caused by noise and blur while struggling with those caused by contrast and fog. To tackle these challenges, we propose a novel image augmentation method grounded in a new perspective of relative spectral differences. This perspective characterizes spectral variations introduced by common corruptions as changes in non-zero frequencies, providing a unified understanding of their effects on image spectra. Building on this insight, the proposed method incorporates two key modules: a random spectral scaling module that captures statistical properties of image spectra and a deep spectral scaling module that adaptively learns spectral adjustments through a neural network. Experiments demonstrate that the proposed method improves overall robustness across various corruptions, with notable gains of 6.3% and 6.4% on contrast and fog, respectively, where existing methods often fall short.