The　performance　of　remote　sensing　images　in　some　applications　is　often　affected　by　the　existence　of　noise,　blurring,　stripes　and　corrupted　pixels,　as　well　as　the　hardware　limits　of　the　sensor　with　respect　to　spatial　resolution.　This　paper　presents　a　universal　reconstruction　method　that　can　be　used　to　improve　the　image　quality　by　performing　image　denoising,　deconvolution,　destriping,　inpainting,　interpolation　and　superresolution　reconstruction.　The　proposed　method　consists　of　two　parts:　a　universal　image　observation　model　and　a　universal　image　reconstruction　model.　In　the　observation　model,　most　degradation　processes　in　remote　sensing　imaging　are　considered　in　order　to　relate　the　desired　image　to　the　observed　images.　For　the　reconstruction　model,　we　use　the　maximum　a　posteriori　(MAP)　framework　to　set　up　the　minimization　energy　equation.　The　likelihood　probability　density　function　(PDF)　is　constructed　based　on　the　image　observation　model,　and　a　robust　Huber-Markov　model　is　employed　as　the　prior　PDF.　Experimental　results　are　presented　to　illustrate　the　effectiveness　of　the　proposed　method.　©　2010　Elsevier　B.V.　All　rights　reserved.