Photoacoustic imaging (PAI) is an emerging noninvasive imaging modality combining the advantages of ultrasound imaging and optical imaging. Image reconstruction is an essential topic in photoacoustic imaging, which is unfortunately an ill-posed problem due to the complex and unknown optical/acoustic parameters in tissue. Conventional algorithms used in photoacoustic imaging (e.g., delay-and-sum) provide a fast solution while many artifacts remain. Convolutional neural network (CNN) has shown state-of-the-art results in computer vision, and more and more work based on CNN has been studied in medical image processing recently. In this paper, we propose Y-Net: a CNN architecture to reconstruct the PA image by integrating both raw data and beamformed images as input. The network connected two encoders with one decoder path, which optimally utilizes more information from raw data and beamformed image. The results of the simulation showed a good performance compared with conventional deep-learning based algorithms and other model-based methods. The proposed Y-Net architecture has significant potential in medical image reconstruction beyond PAI.