Rain　streaks　typically　cause　significant　visual　degradation　and　foreground　occlusions,　hindering　the　progress　of　visual　tasks　in　outdoor　scenarios.　Existing　image　deraining　methods,　predominantly　based　on　Convolutional　Neural　Networks　(CNNs),　exhibit　certain　limitations.　These　methods　tend　to　overly　focus　on　low-level　visual　features,　demonstrating　insufficient　ability　to　capture　high-dimensional　global　features.　Furthermore,　they　often　lack　targeted　attention　to　channel　information　and　spatial　details,　which　restricts　their　effectiveness.　To　address　these　shortcomings,　this　paper　proposes　the　Delta-Calibration　Derain　Network　(DCD-Net).　The　DCD-Net　introduces　a　sequential　Delta　Convolutional　Layer　structure　to　significantly　expand　the　feature　acquisition　range.　Additionally,　this　study　pioneers　the　Joint　Calibration　Attention　module,　which　precisely　captures　both　channel　and　spatial　feature　information,　leading　to　enhanced　network　performance.　Experimental　results　across　multiple　synthetic　datasets　show　that　the　proposed　method　achieves　superior　performance　in　terms　of　Peak　Signal-to-Noise　Ratio　and　Structural　Similarity　Index,　validating　the　advantages　of　DCD-Net　over　traditional　CNN-based　models.