Remote　sensing　super-resolution　(SR),　which　aims　to　reconstruct　high-resolution　(HR)　images　with　rich　spatial　details　from　low-resolution　(LR)　remote　sensing　images　predominantly　composed　of　low-frequency　components,　presents　a　challenging　yet　practical　task.　Existing　diffusion　model　(DM)-based　methods　for　remote　sensing　SR　are　inefficient,　requiring　extensive　iterations　and　often　failing　to　recover　high-frequency　details　adequately　due　to　a　lack　of　targeted　processing　for　high-frequency　components.　To　mitigate　these　challenges,　this　article　introduces　an　efficient　DM　for　remote　sensing　image　SR,　termed　image　reconstruction　representation-diffusion　model　for　super-resolution　(IRR-DiffSR).　IRR-DiffSR　employs　a　feature　extraction　encoder　to　extract　the　image　reconstruction　representation　(IRR)　from　ground-truth　(GT)　images,　which　makes　the　reconstruction　network　focus　more　on　recovering　high-frequency　textures.　Unlike　traditional　DM-based　methods　that　learn　the　direct　mapping　from　LR　to　HR　images,　IRR-DiffSR　employs　a　pre-trained　encoder　to　guide　the　DM　in　extracting　consistent　IRR　directly　from　LR　images.　This　auxiliary　information　aids　in　the　efficient　and　effective　reconstruction　of　high-frequency　textures.　By　serving　as　an　implicit　reconstruction　prior,　this　enables　the　DM　to　achieve　accurate　estimations　with　fewer　iterations,　thus　assisting　IRR-DiffSR　in　recovering　high-frequency　information　more　efficiently　and　effectively.　Extensive　experiments　on　four　remote　sensing　datasets　demonstrate　that　IRR-DiffSR　achieves　state-of-the-art　reconstruction　results　in　both　real　and　synthetic　scenarios.　Specifically,　in　real　scenarios,　IRR-DiffSR　outperforms　the　next　best　method　by　0.766　and　0.69　in　the　naturalness　image　quality　evaluator　(NIQE),　while　in　synthetic　scenarios,　it　achieves　peak　signal-to-noise　ratio　(PSNR)　improvements　of　1.07　and　0.51.　These　results　highlight　the　effectiveness　and　efficiency　of　IRR-DiffSR　in　recovering　high-frequency　details.　©　1963-2012　IEEE.