13:50 〜 14:10
[DES6-2 (Invited)] A Cost-effective Burn-in Compensation Method Using Deep Convolutional Networks with Detail Layer Accumulation
OLED burn-in, Data-counting, Deep convolutional network, Multi-unit compensation
In this paper, a cost-effective burn-in compensation method based on optical imaging is proposed. A deep convolutional networks using the accumulated detail layer as a reduced reference is applied to the captured images. Experimental results show that the proposed method reconstructs burn-in details effectively to be followed by multi-unit compensation.