Image Quality Is Not All You Want: Task-Driven Lens Design for Image
  Classification
Xinge Yang Qiang Fu Yunfeng Nie Wolfgang Heidrich
In computer vision, it has long been taken for granted that high-quality
images obtained through well-designed camera lenses would lead to superior
results. However, we find that this common perception is not a
"one-size-fits-all" solution for diverse computer vision tasks. We demonstrate
that task-driven and deep-learned simple optics can actually deliver better
visual task performance. The Task-Driven lens design approach, which relies
solely on a well-trained network model for supervision, is proven to be capable
of designing lenses from scratch. Experimental results demonstrate the designed
image classification lens (``TaskLens'') exhibits higher accuracy compared to
conventional imaging-driven lenses, even with fewer lens elements. Furthermore,
we show that our TaskLens is compatible with various network models while
maintaining enhanced classification accuracy. We propose that TaskLens holds
significant potential, particularly when physical dimensions and cost are
severely constrained.