Many　pieces　of　research　have　been　conducted　on　image-restoration　techniques　to　recover　high-quality　images　from　their　low-quality　versions,　but　they　usually　aim　to　handle　a　single　degraded　factor.　However,　captured　images　usually　suffer　from　various　degradation　factors,　such　as　low　resolution　and　compression　distortion,　in　the　procedures　of　image　acquisition,　compression,　and　transmission　simultaneously.　Ignoring　the　correlation　of　different　degraded　factors　may　result　in　the　limited　efficiency　of　the　existing　image-restoration　methods　for　captured　images.　A　joint　deep-network-based　image-restoration　algorithm　is　proposed　to　establish　a　restoration　framework　for　image　deblocking　and　super-resolution.　The　proposed　convolutional　neural　network　is　made　up　of　two　stages.　A　deblocking　network　is　constructed　with　two　cascade　deblocking　subnets　first,　then,　super-resolution　is　performed　by　a　very　deep　network　with　skipping　links.　Cascading　these　two　stages　forms　a　novel　deep　network.　An　end-to-end　training　scheme　is　developed,　which　makes　the　two　stages　be　trained　jointly　so　as　to　achieve　better　performance.　Intensive　evaluations　have　been　conducted　to　measure　the　performance　of　the　authors＇　method　both　in　general　images　and　face　images.　Experimental　results　on　several　datasets　demonstrate　that　the　proposed　method　outperforms　other　state-of-the-art　methods,　in　terms　of　both　subjective　and　objective　performances.