Parasitic Egg Detection With A Deep Learning Ensemble

Depth estimation and all-in-focus image restoration from defocused RGB images are related problems, although most of the existing methods address them separately. The few approaches that solve both problems use a pipeline processing to derive a depth or defocus map as an intermediary product that serves as support for image deblurring, which remains the primary goal. in this paper, we propose a new Deep Neural Network (DNN) architecture that performs in parallel the tasks of depth estimation and image deblurring, by attaching them the same importance. Our Two-headed Depth Estimation and Deblurring Network (2HDED:NET) is an encoder decoder network for Depth from Defocus (DfD) that is extended with a deblurring branch, sharing the same encoder. The network is tested on NYU-Depth V2 dataset and compared with several state-of-the art methods for depth estimation and image deblurring.