Adaptive image enhancement for robust CNN classification under low illumination

Abstract

Convolutional Neural Networks (CNNs) are widely used in image classification tasks, but their performance can degrade significantly under poor illumination conditions. Although numerous image enhancement methods have been developed to mitigate this issue, identifying the most appropriate technique for a specific image remains challenging. This work aims to determine the most effective image enhancement algorithm for a potentially dimmed input image to enhance the classification prediction performance. To do this, a trained regressor, which evaluates different features of an image, ascertains the increase or decrease in classification prediction perfor- mance between the input image and the enhanced image. These predictions are then used to select the most suitable image enhancement algorithm to be applied to the input image for optimal CNN classification. Exper- imental results using various CNN architectures and enhancement techniques demonstrate that the proposed strategy consistently improves classification performance under challenging lighting conditions.