Kernel Filters

• sharpen and blur images
• These filters work by sliding an n × n matrix across an image with either a Gaussian blur filter, which will result in a blurrier image, or a high contrast vertical or horizontal edge filter which will result in a sharper image along edges
• Intuitively, blurring images for Data Augmentation could lead to higher Resistance to motion blur during testing
• Additionally, sharpening images for Data Augmentation could result in encapsulating more details about objects of interest.
• Kang et al. experiment with a unique kernel filter that randomly swaps the pixel values in an n×n sliding window. They call this augmentation technique PatchShuffle Regularization