In　this　paper,　we　present　a　deep　correlated　image　set　compression　scheme　based　on　Distributed　Source　Coding(DSC)　and　multi-scale　image　fusion.　As　there　exists　strong　correlation　among　images　in　a　similar　image　set,　we　propose　to　utilize　such　correlation　to　generate　side　information　at　decoder　side　for　each　image　in　the　set.　Specifically,　a　reference　structure　of　the　image　set　is　generated　by　building　a　minimum　spanning　tree　according　to　the　similarity　between　two　images　at　encoder.　With　the　reference　structure,　the　side　information　of　each　image　to　be　decoded　can　be　generated　based　on　the　decoded　reference　image.　And　our　network　learns　the　correlation　between　an　image　and　its　side　information　in　the　training　phase.　Based　on　the　principle　of　DSC,　the　side　information　can　provide　additional　information　such　as　rich　details　at　decoder　side.　To　make　full　use　of　the　side　information,　the　initially　decoded　image　and　the　additional　side　information　are　fused　at　different　scales.　A　decompressed　image　enhancement　network　is　introduced　to　reduce　the　compression　artifacts　of　the　decoded　images.　Extensive　experimental　results　compared　with　other　mainstream　methods　validate　the　superior　performance　of　our　scheme　in　both　terms　of　subjective　and　objective　quality.　©　2022　IEEE.