A Quaternion Monogenic Layer Resilient to Large Brightness Changes in Image Classification

Conventional CNN cannot guarantee the invariance response even to small changes in input images, for example, with brightness variations. This paper analyzes the performance of a novel convolutional layer in the Fourier domain capable of classifying images even with large alterations of their brightness. The results show that the performance of a given CNN architecture is significantly more resilient to large brightness changes if use the proposed layer.