Kernel Functions in CNNs: Enhancing Robustness to Image Noise

Convolutional Neural Networks (CNNs) have achieved remarkable success across various fields, particularly in image processing. However, the interference from imaging devices and external environments introduces unavoidable noise, leading to a degradation in image quality. This paper proposes a Kernel-based Convolutional Neural Network (K-CNN) to enhance CNN robustness against image noise by incorporating kernel functions into convolution operations. The proposed K-CNN utilizes different types of kernel functions to improve nonlinear feature extraction without increasing model complexity. Experiments on the CIFAR-10 dataset demonstrate that K-CNN significantly outperforms standard CNNs in handling Salt-and-Pepper and Gaussian noise. The results highlight the potential of kernel-based convolution operations as an alternative approach for improving CNN robustness in real-world noisy environments.