  Convolutional Neural Networks (ConvNets) at present achieve remarkable
performance in image classification tasks. However, current ConvNets cannot
guarantee the capabilities of the mammalian visual systems such as invariance
to contrast and illumination changes. Some ideas to overcome the illumination
and contrast variations usually have to be tuned manually and tend to fail when
tested with other types of data degradation. In this context, we present a new
bio-inspired {entry} layer, M6, which detects low-level geometric features
(lines, edges, and orientations) which are similar to patterns detected by the
V1 visual cortex. This new trainable layer is capable of coping with image
classification even with large contrast variations. The explanation for this
behavior is the monogenic signal geometry, which represents each pixel value in
a 3D space using quaternions, a fact that confers a degree of explainability to
the networks. We compare M6 with a conventional convolutional layer (C) and a
deterministic quaternion local phase layer (Q9). The experimental setup {is
designed to evaluate the robustness} of our M6 enriched ConvNet model and
includes three architectures, four datasets, three types of contrast
degradation (including non-uniform haze degradations). The numerical results
reveal that the models with M6 are the most robust in front of any kind of
contrast variations. This amounts to a significant enhancement of the C models,
which usually have reasonably good performance only when the same training and
test degradation are used, except for the case of maximum degradation.
Moreover, the Structural Similarity Index Measure (SSIM) is used to analyze and
explain the robustness effect of the M6 feature maps under any kind of contrast
degradations.
