As　the　primary　pollutant　in　China＇s　urban　atmosphere,　PM$_{2.5}$　poses　a　great　threat　to　the　health　of　residents　and　ecological　stability.　Efficient　and　effective　PM$_{2.5}$　concentration　monitoring　is　essential.　Nonetheless,　the　popular　devices　for　PM$_{2.5}$　monitoring　are　developed　based　on　two　standards:　the　micro-oscillation　balance　method　and　the　$\beta$-ray　method,　which　have　high　purchase　and　maintenance　costs　and　slow　calculation　rates.　To　this　end,　we　put　forward　a　real-time　and　reliable　vision-based　estimation　algorithm　of　PM$_{2.5}$　concentration.　To　be　specific,　the　proposed　method　first　develops　two　natural　scene　statistical　analysis-based　visual　priors　to　measure　saturation　and　structural　information　losses　caused　by　the　‘haze’　formed　by　PM$_{2.5}$.　Moreover,　we　develop　a　lightweight　deep　belief　network　(DBN)-deep　neural　network　(DNN)-based　PM$_{2.5}$　concentration　estimation　model,　which　learns　the　mapping　from　the　designed　visual　priors　to　PM$_{2.5}$　concentrations.　Experiments　confirm　the　superiority　of　our　vision-based　PM$_{2.5}$　concentration　estimation　method　by　comparison　with　state-of-the-art　photo-based　PM$_{2.5}$　monitoring　methods.　IEEE