Reflection　removal　is　a　crucial　issue　in　image　reconstruction,　especially　for　high-definition　images.　Removing　undesirable　reflections　can　greatly　enhance　the　performance　of　various　visual　systems,　such　as　medical　imaging,　autonomous　driving,　and　security　surveillance.　However,　the　resolution　of　existing　reflection　removal　datasets　is　not　high　and　the　training　data　heavily　relies　on　synthetic　data,　which　hampers　the　performance　of　reflection　removal　methods　and　restricts　the　development　of　effective　techniques　tailored　for　high-definition　images.　Therefore,　this　paper　introduces　a　new　dataset,　Real-world　Reflection　Removal　in　4K　(RR4K).　This　novel　dataset,　with　its　large　capacity　and　high　resolution　of　6000x　4000　pixels,　represents　a　significant　advancement　in　the　field,　ensuring　a　realistic　and　high　quality　benchmark.　Furthermore,　building　upon　the　dataset,　we　propose　an　efficient　method　for　single-image　reflection　removal,　optimized　for　high-definition　processing.　This　method　employs　the　U-Net　architecture,　enhanced　with　large　kernel　distillation　and　scale-aware　features,　enabling　it　to　effectively　handle　complex　reflection　scenarios　while　reducing　computational　demands.　Comprehensive　testing　on　the　RR4K　dataset　and　existing　low-resolution　datasets　has　demonstrated　the　method＇s　superior　efficiency　and　effectiveness.　We　believe　that　our　constructed　RR4K　dataset　can　better　evaluate　and　design　algorithms　for　removing　undesirable　reflection　from　real-world　high-definition　images.　Our　dataset　and　code　are　available　at　https://github.com/jengchauwei/RR4K.