Deep　unfolding　networks　have　gained　increasing　attention　in　the　field　of　compressed　sensing　(CS)　owing　to　their　theoretical　interpretability　and　superior　reconstruction　performance.　However,　most　existing　deep　unfolding　methods　often　face　the　following　issues:　(1)　they　learn　directly　from　single-channel　images,　leading　to　a　simple　feature　representation　that　does　not　fully　capture　complex　features;　and　(2)　they　treat　various　image　components　uniformly,　ignoring　the　characteristics　of　different　components.　To　address　these　issues,　we　propose　a　novel　wavelet-domain　deep　unfolding　framework　named　WTDUN,　which　operates　directly　on　the　multi-scale　wavelet　sub-bands.　Our　method　utilizes　the　intrinsic　sparsity　and　multi-scale　structure　of　wavelet　coefficients　to　achieve　a　tree-structured　sampling　and　reconstruction,　effectively　capturing　and　highlighting　the　most　important　features　within　images.　Specifically,　the　design　of　tree-structured　reconstruction　aims　to　capture　the　inter-dependencies　among　the　multi-scale　sub-bands,　enabling　the　identification　of　both　fine　and　coarse　features,　which　can　lead　to　a　marked　improvement　in　reconstruction　quality.　Furthermore,　a　wavelet　domain　adaptive　sampling　method　is　proposed　to　greatly　improve　the　sampling　capability,　which　is　realized　by　assigning　measurements　to　each　wavelet　sub-band　based　on　its　importance.　Unlike　pure　deep　learning　methods　that　treat　all　components　uniformly,　our　method　introduces　a　targeted　focus　on　important　sub-bands,　considering　their　energy　and　sparsity.　This　targeted　strategy　lets　us　capture　key　information　more　efficiently　while　discarding　less　important　information,　resulting　in　a　more　effective　and　detailed　reconstruction.　Extensive　experimental　results　on　various　datasets　validate　the　superior　performance　of　our　proposed　method.