Haze　reduces　the　imaging　effectiveness　of　outdoor　vision　systems,　significantly　degrading　the　quality　of　images;　hence,　reducing　haze　has　been　a　focus　of　many　studies.　In　recent　years,　decoupled　representation　learning　has　been　applied　in　image　processing;　however,　existing　decoupled　networks　lack　a　specific　design　for　information　with　different　characteristics　to　achieve　satisfactory　results　in　dehazing　tasks.　This　study　proposes　a　heterogeneous　decoupling　unsupervised　dehazing　network　(HDUD-Net).　Heterogeneous　modules　are　used　to　learn　the　content　and　haze　information　of　images　individually　to　separate　them　effectively.　To　address　the　problem　of　information　loss　when　extracting　the　content　from　hazy　images　with　complex　noise,　this　study　proposes　a　bi-branch　multi-hierarchical　feature　fusion　module.　Additionally,　it　proposes　a　style　feature　contrast　learning　method　to　generate　positive　and　negative　sample　queues　and　construct　contrast　loss　for　enhancing　decoupling　performance.　Numerous　experiments　confirm　that　the　proposed　algorithm　achieves　higher　performance　according　to　objective　metrics　and　a　more　realistic　visual　effect　when　compared　with　state-of-the-art　single-image　dehazing　algorithms.