Deeply Cascaded U-Net for Multi-Task Image Processing

Understand

In current practice, many image processing tasks are done sequentially (e.g.

• denoising, dehazing, followed by semantic segmentation).
• In this paper, we propose a novel multi-task neural network architecture designed for combining sequential image processing tasks.
• We extend U-Net by additional decoding pathways for each individual task, and explore deep cascading of outputs and connectivity from one pathway to another.