Deep　unfolding　network　(DUN)　is　a　powerful　technique　for　image　compressive　sensing　that　bridges　the　gap　between　optimization　methods　and　deep　networks.　However,　DUNs　usually　rely　heavily　on　single-domain　information,　overlooking　the　inter-domain　dependencies.　Therefore,　such　DUNs　often　face　the　following　challenges:　1)　information　loss　due　to　the　inefficient　representation　within　a　single　domain,　and　2)　limited　robustness　due　to　the　absence　of　inter-domain　dependencies.　To　overcome　these　challenges,　we　propose　a　deep　unfolding　framework　D^3U-Net　that　establishes　a　dual-domain　collaborative　optimization　scheme.　This　framework　introduces　both　visual　representations　from　the　image　domain　and　multi-resolution　analysis　provided　by　the　wavelet　domain.　Such　dual-domain　representations　constrain　the　feasible　region　within　the　solution　space　more　accurately.　Specifically,　we　design　a　consistency-difference　collaborative　mechanism　to　capture　inter-domain　dependencies　effectively.　This　mechanism　not　only　enhances　the　fidelity　of　reconstruction　but　also　enriches　the　depth　and　breadth　of　extracted　features,　improving　the　overall　robustness　and　reconstruction　quality.　Moreover,　we　develop　an　inter-stage　transmission　pathway　to　minimize　the　information　loss　during　transmission　while　broadcasting　multi-scale　features　in　a　frequency-adaptive　manner.　Extensive　experimental　results　on　various　benchmark　datasets　show　the　superior　performance　of　our　method.　©　2024　ACM.