Recently,　deep　neural　networks　have　attracted　great　attention　in　photoacoustic　imaging　(PM).　In　PM,　reconstructing　the　initial　pressure　distribution　from　acquired　photoacoustic　(PA)　signals　is　a　typically　inverse　problem.　In　this　paper,　an　end-to-end　Unet　with　residual　blocks　(Res-Unet)　is　designed　and　trained　to　solve　the　inverse　problem　in　PAI.　The　performance　of　the　proposed　algorithm　is　explored　and　analyzed　by　comparing　a　recent　model-resolution-based　regularization　algorithm　(MRR)　with　numerical　and　physical　phantom　experiments.　The　improvement　obtained　in　the　reconstructed　images　was　more　than　95%　in　Pearson　correlation　and　39%　in　peak　signal-to-noise　ratio　in　comparison　to　the　MRR.　The　Res-Unet　also　achieved　superior　performance　over　the　state-of-the-art　Unet++　architecture　by　more　than　18%　in　PSNR　in　simulation　experiments.　(C)　2020　Optical　Society　of　America　under　the　terms　of　the　OSA　Open　Access　Publishing　Agreement