The current standard photoacoustic (PA) technology is based on heavy, expensive and hazardous laser system for excitation of a tissue sample. As an alternative, light emitting diode (LED) offers safe, compact and inexpensive light source. However, the PA images of an LED-based system significantly suffer from low signal-to-noise-ratio due to limited LED-power. With an aim to improve the quality of PA images, in this work we propose to use deep convolutional neural networks that is built upon a previous state-of-the-art image enhancement approach. The key contribution is to improve the optimization of the network by guiding its feature extraction at different layers of the architecture. In addition to using a high quality target image at the output of the network, multiple target images with intermediate qualities are employed at in-betweens layers of the architecture to guide the feature extraction. We perform an end-to-end training of the network using a set of 4,536 low quality PA images from 24 experiments. On the test set from 15 experiments, we achieve a mean peak signal-to-noise ratio of 34.5 dB and a mean structural similarity index of 0.86 with a gain in the frame rate of 6 times compared to the conventional approach.