Local Phase U-net for Fundus Image Segmentation

In this paper, we propose Rectified Local Phase Unit (ReLPU), which is an efficient and trainable convolutional layer that utilizes phase information computed locally in a window for every pixel location of the input image. The ReLPU layer is based on applying the Rectified Linear Unit (ReLU) activation function on the local phase information extracted by computing the local Fourier transform of the input image at multiple low frequency points. The ReLPU layer, when used at the top of the segmentation network U-Net, is observed to improve the performance of the baseline U-Net model. We demonstrate this using the task of segmenting blood vessels in fundus images of two standard datasets, DRIVE and STARE, achieving state-of-the-art results. An important feature of the ReLPU layer is that it is trainable which allows it to choose the best frequency points for computing local Fourier transform and to selectively give more weight to them during training.