Problem 909. Image Processing 003: Interferometer Data Interpolation

Created by Richard Zapor

Solve Later

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version=\"1.0\" encoding=\"UTF-8\"?>\n<w:document xmlns:w=\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\"><w:body><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>This Challenge is to correct a 2-D Interferometer data set for drop-outs.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>Interferometer data is notorious for drop-outs which result in values of zero.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>Examples of Pre and Post WFE drop-out correction:</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId1\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId2\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>surf layed flat</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId3\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId4\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>3-D surf</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId5\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Challenge WFE :Pre Drop-Out insertion</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Input:</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>2-D square array representing surface z-values(NaNs in WFE array)</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>Corrupted data points(Drop-Outs) are represented by having the value 0.000</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Output:</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>same size 2-D square array as input with Drop-Outs corrected, approximately.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Hint:</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>TriScatteredInterp allows for interpolation onto a grid given a non-grid set of (x,y,z) points.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Follow-Up:</w:t></w:r><w:r><w:t> Re-sampling to a grid, Convolving with an Optical Transfer Function and finally Calculating MTF</w:t></w:r></w:p></w:body></w:document>"},{"partUri":"/matlab/output.xml","contentType":"text/xml","content":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\" ?><embeddedOutputs><metaData><evaluationState>manual</evaluationState><layoutState>code</layoutState><outputStatus>ready</outputStatus></metaData><outputArray type=\"array\"/><regionArray 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"},{"partUri":"/media/image2.JPEG","contentType":"image/JPEG","content":"data:image/JPEG;base64,/9j/4AAQSkZJRgABAgAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADCAPoDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiikzQAYo6dKp3eo2tkubidE9ATkn8K5vUPGixsyWkWeOHf/AArelh6lV+6jgxWZYbDfxJa9up15IHU1UuNUsrVcz3UKAddzgV5tfeI727XEl0wH91TtH6f1rDe6+boPxr0aWUTkrzdjw6vEybtRp39T1Cbxno0XHns5H91D/Os+f4gWif6qzmcY7kD/ABrz4zBv4qYZQFb6da7Y5RRW92cE+IMZLay+R27fEOUp8mnKpPQtNn+lMX4g3LcfY4c/75/wrz+a429O1QPfr7CuuOU0GtI/mR/aePlqp/kekD4hXKMu+xiK99rnNSD4korfPpxC9yJf/rV5ZJqny4JGPaq76mvr+tWskoPeP5m9PMMw6zv8kewQ/E3SW2ia2uoyepChgP1z+la9n4z0C94j1GJG/uy5Q/rXz82p+4xT4Z97feqKnD1Jq8W0d8M1xUVeaTPpuOVJV3o6sp6FWyDT818/6Zqt3pzeZaXEkTf7DcH6jofxrudJ8b6iuxLoR3C45bG1j+XH6V42IyetS1i7o6aef0Nqq5fxPR6KyLTX7O5C7nMTHoJBitVWVlypBB9K8mUXF2Z69HE0q8eanJMfRRRSNwooooAKKKKACiiigAooooAKKKKAEopCcLzWDqWubN0NrgsOC/YfShtdTmxOKp4aHPUZpXupW1kv72QBiOFHJNclqvie5ZWEJEEftyx/wqrM29mJJLE5JPesjUCNreuK9fA4aErOSufCZjn+Irz9nSfLHy3+8zrvUD8zEkk9STyaypdR396ZdB2buBmqJiPp+dfU0qMEjmpUYvWW5d+1D++c00vvbqarhPm4H1NOrXlXQ05EtiyG2+1I0wK9e1QM+3r+tV3fLe1ChdjjTuTO6been1rPuJ419M0krnbWVcvlsgHiuqlS1O2hQTe5JcTDb/8AXqg8p7E4prs/ccUIu/nmu+MFFHpwgoocrF1X1qxDI0T96riMryD3qzED0I4pTtYU7WNu0n39a6CwuNsisD0Ncvaso9a2bZX2qR0rysRBO54mKpp3O6trvzV+frWra309u2YZCvbHb8q4i0u3T7wrbg1BGXk18xjMu59YnlU61bCz5oM76x12Kb5LjEb+v8Na4YMMg5rzZL1G/i68Vq6frU9lx/rIumwnp9K8eeEqw3R9Tl/EsW1DEfedt+FBqnZX8F9Hvib6qeoq53rneh9bTqRqRUoO6YtFFFIsKKKKACiiigBKa7hRuJwBzmhmCqSeAOpNYOpXzTHZGcRj/wAerkxWLhh1q9XsjOrUVOLkyPUtRe4/dxsVizzj+KsaQqKlmb5az55tq08uoVcRPnmz4TN8fzSd9RssoWs25w3vntUdxdZ6nvVbz9zck19rRociVj5uMJN8zImtC/zbOPWoTZxr1HPWtJH3rtH3aeVDR8AZ710+0ktDZVpLRnPTQlfuCqskXHPX61vT23y9QKzZIHXoM/7R7V1U6qaO2lWTW5n+RnuTxzSmEBen41aWDZ1bryWqTywegI+ta+0NnU8zFni/+v71Ta13djW/NAnYgnPSqjqFXAA68+1bwrO2h006ztoYr2melAtAO1XX+831pUG5sV0e0djp9rKxUFqPSpY7L5uKvRxj/GtG3tt3asZ12kYVMS4op2+nh+1atvaPF06Vr2NgPvMKviyUNx930Iryq2NV7M8etjnJtGCwDL6VUkmki+7XSXGnxMvygBqyJrR4m2uOKKVWMiaVaEiG3vD68iulsZfNjXJrATT/AJtyDNb+nwmKPBrmxrh7NtGdfkbXLubFm7wzK8blWB6jv9a66wvBdQ/PgOOoFcrapzV6af7JHuRtsmMgjtX5ZmOdPCYlJap7o/Q+HcPUlTstjqKKytH1mLVoWAIWaM4kTPT3+latfQUasa0FOGzPcnCUJOMlZi0UUVqSJRRisvXdVTSNNe5ON+dqKe7Gs6lRU4OctkVCEpyUY7sdfXO7dCh/3qx5lNYVtq5eTJkJZjkk963oJluY+cZ9fWvzfE5jVli3WqbdPJHVjsrmqdupmyg1m3UZK7QK6GW39qpyW3tX3GT57Rikfm+Z5PWvddDkri0k+9jj3qFYCzV1Elv7ZqnJbDtxX3GGzGFZaHztT2lHSaM2GF1bA6VoJboq89fWgxbeFqUH5a1nNvY451HIpy243VUmtd64OcZzitGRgWpgI9RirjNouFSS1MiOxZpuRkA8CmzR7eNmPatTz0hkz1BP5VXnlt5Gzn8+1bRqSbu1odMas29Voc9eLGnIIz3rKlbD5x1rXvQPmAHBP3qyLpoui7mYdlFepR2R7OH1SKxcbsd6er7GqlPlWyM/jULzFWzvxmu1QutDvVK60N2KZFrYs7uNVX9K4db0+ZsJwa07O6LMvz9qwrYZtanPiMG2tT0KxvlZtvathWBXiuIspSNuDXVWExdVDenWvncdh0tUfP1qXs56Fp1DduagktfN+9V9F3U8xg14rzH2bsio4OUveTM6C18rirscVS+XuqWNa48bmjlDQ68NgL1LyLVoPu1Q12Qru5xgcVowcNWb4hQsuTyCK/MMyfPi1J9T9V4aSSUWcJH4gn0bVYb6Llom+Zf7ynqP8969p0XV7bXdJt9RtCTFMuQD1U9wfQg8V8+638u7+VdH8JvFKWOsy6FdTkQ3ZDW+9vlSQdQP94fqPevrMoq8seToz7PPMthUwqxFNe8vy/4B7jRSClr3z4gSvHfiD4jN9r32CBx5Fp8pIP3nPX8v8a9R1vUV0nRbu/faRDEzgMcZIHA/E183m9kurqS4lctJKxZmPck5NeZmc3yezXU+o4ZwKrVZV5LSO3qzp9NuyzZJPX1rtdPvAI1z0rzuwBbac85rrrF/3a96+Ix1JM9rMaEWdpBOsq+tPkj3Vj2LvW3HkR/NzXixlKjP3WfIYzDQd0zPmhrPlj21sTVmTiv0Th7G1JWTZ+cZ5hIRu0jNmO1aoG529Ku3P3fr+lY06hWznp+tfpdCKlFXPjqUE20yaS4/Cqk10V4z+tVZrrb9az5bnPeu+nQud9LD36F6a9DdzVBruVuF5Hqah8wFqeBziuqNOMUdkacYLYCzuuDkn27VTmgkRmEXU8kntWzDEG/lSSxBGzjPvQqiTshxrcrsjm5dPk8vLP8AN1wK5+7llik8t+nbiu1u5E21yV/F5srSNxjpXo4Wo5P3j18FV537yMtLkozIc9cg1qadc571hTHLM68DoM1f0ond3ODXoVYJwuepXppwbO902bO2uy0piy1xGkIzba7vS4SkeT1NfJZk1FO58TmCSlZGvGKsKtRRLVpEr80zGuoybTPVwNFyitBBHTlTBqULUipXzdXHNJ3eh79LBptWQsaVn67/AKvb7c1qcRRsx7CsPVLgTK3P/wBavnKlV1qyfRH2OUYd02mkeX67/F9TXIStJBcx3ELFZY3Dow7EHIP5122uRnc2enqK5C5T5mr67AytFWP0mNNVcNyn0t4L8SReKfDNrqK4E2PLuEBzskHUfyI9jXRV4x8EdXKz6jor52kfao/Y8K3/ALL3r2evqKU+eKZ+Y4/D/V8RKmjgPi5f/ZvB4tw7K1zOqfL3A+Yg/lXhsJ+bAr1b423TpBo9tkeU7SyMMc5AUD/0I15HC3zda8vHa1D7rheKjg13bf8Al+h0+lHDLXX2DgMvvXC6fJt5z3rpbK92t64r5nGU3Jux14+i5N2O+sl+7xWuflj/AArmdOui+0k4H1rpAd8P4V83Ujy1VzHxGYQlFMrymqE/3avSD5apyrX22TSjFp3PzzNouSaMa8QnkcYrAuWb5q6uWP72ay7m0XbwOP5V+o4HExlBHxF/ZTakcVqEro1Z3nnucH0rotRst3QVgXFo6djn6V9HQnCSR7mGnCUUhyS7utXITnb7VnRwye9aluhXqOfpVVLJaFVrJaGlaqflPTBzzUkzheg4606BDtp0sBZea89yXMeW5Lm1Ma78tl6AHrXNahEX3Z5C/wAI7118tlv7VmT6aMYxzmu/D1YxZ6mFxEYPc4l7J9y4XLdh2FdDpOmybV+Qe3HX3rbj0YPIvy849K6Gw0jb0AHHFXicxXLY1xearksiHRrA/LkfpXWW8WxVAqC1tPK/HritSGKvhs5zJJNJnk4alPE1eZrQfElWkSkjjqwq1+X5jj7t2Z9xgcFZK6GqtSKKULTuFXJr56riLo92lh7NIiuji3YeorlL843cfWukuJyyt6Vz9ym9+cc1OG0d2fRYGPJucXqkJbdjv1rkby3KSV6TfWm5W4Fcve2Q3c19Ng8QkrH2GCxCceVkHgC/fR/G2nTYOyZ/s7jHZ+P54NfSg6V8xW8MkGp2s0RKukyFWHYgjBr6bX7o+lfU4Cp7SDXY+V4poxjXhUj1X5f8OeOfHXKnQ2wcfvhnHH8FeRQy17n8cLRpvB9rcK4C292pYHvkMP614EjbWpYqF53OzIcQ1hkl0N61m29+K6OwcN/H36Vx1tL81bVnc7WXHXPNeNiKV07H1c17WndHpWlsW212Fnn7P+lcJoM+/bk54rvbUf6PnPWvj8cuWdj4bNo8raZHKKqvVyUVUcGvo8sn7qufnGYQ1dipItUpY81pOtVmjr7nLcYoqzZ8dj8M27pGFdxbuNnSsiWwL8lePUV1klvv6igWi7duyvpqeYU4RTucFOdWGiRx0OnN5nCcZrSg0x2Zfl49a6FbRF6IB+FSC3qKmcU+jNpPET2RmpYIq082Qb8q01gpTBXmyziCdriWBrtXZhzWA28fpVT+yXLZ2dfUV0/2Ymni19qbz+lBWua08FitkYcOnhdu8dPStCGEIvFXDbUiwmuatnMKsXZ2GsvqqSc1cI1q9FHUCRbavQpXxWc46ybiz6rKsG7pNEkaVJtHrSgbarSyhdxzzmvhalaVSbaZ9tQwySSsTNKqr/jUTzoy1TeXcvU8Um8t0FQodzujRSFd06E1kXUgEnArRmh+XjrVF4dzc/erqpWWp20eValCcF16dawrq13Sc/pXWSWxC8/hVN7HdzjOa7KNdRPRoYlQOZt7ENcR/J/Gp+nIr3dfuj6V5jb6cvnKeBggj869OHQV9fw/X51U+R4ue1/bSh5X/QwPG2lrrPgzVbMpl2t2aP8A31GV/UV8mqdrV9pHmvlX4g+HP+Ec8Y3lpGrLbyHz4MnPyN/gQR+FeziY6XJyWs1KVP5mJbye9bFrKFbj0rAiG2tO1c15VaCaPvcDUbXIzudE1Ioy545r0bTNVRoVV8kGvINPO1txNdlpl1tXqc+lfMZhhlJ3R5ma4KNS7PQ8JKu5DmoWi9qybC+f+/29K2o50defzrzKeKqYd26HwmNypN6Fcxe1J9nq/sU+lIY9vSu2OcySsnY8eWUxvqih9lHpTDBjtWkFpGjGK3p55VvaTMJ5PTaukZwiFPWMVa2Cm7RXT/aUqibTOdZeoPVDBEKXyRUgFSqtefWxtSOtzvpYSEtLFfycUhj9qt7KMpXIs0kt9Tp/s2L20KnlUnl4q7tWm+XWkc0b6kSy1LoVlT1qwg9KQgU5SKwxOIdVXNcPQVN2I55Cq8fSqcg37f1NaLKrdaaI0XsOetefF22R7EasYorRW4ZuRkAdamNsm7rUhYL04FRPJ710U8PUqPQ5a2OUNbimCNuOahNkqtwKd5tHn+9dLy6t9k5oZrFL3mRTWm5enNVlsn6447VorOO/8qUzjtWawuIT5bHUs1pqN7hYaerXMZbHynJH0rpMVl6SN5kmP0H9a1a+/wAgwcsPhff3k7nNUruv7wV5v8XfC76xoMepWsDSXdiSWCKMtEfve5xgEY9+K9IprKGUqQCCMEGvcnFSjYrD1pUKsakeh8eJHj6Vcgwtdj8RvCB8Pa4Z7WIjTrti0RHRG/iX29R7cdq5GOOvErJwbiz9Ry6pCvTjVp7M3LIo61vWspiZcDiuesTs4610VtGZFXFeJibX12Hi7X12Ols5kdV2v8wrXtLjPBOfauXtkeJuvNbtiBL7GvCxFNHzWJpJXZ0dsfxz+lWTWfaJIGw/6Vdc15ihedkfPYlqGo4kUxnqJ3qIy46V6NDATkrrU8mtjYxdticmmZqEzUCQe1ejHBVIq9jgljISdrlgGpFeqnmU5Zayq4CclexrSxsIu1y3voJH0+lVvMFIZa4f7Nm3sdn9oRS3LYIprNVfzqb51XDLJ31RE8xg1uTlhSB6qtNTfPr0oZVNxWh588ygnuXfMpDJVEz5pTPVrJp3V0S82g07MneTFQPNULy+9U57jauf1r6LLslc2lY8LHZtyp2ZZa4296jN0PWsK71dIujVjS6427h/1r7PD8Owa1R4f1vFVXeOx24u/en28r3NzHBHyXOPp6mvO5fEZUff7eteoeBtNmXS11G7z51yoaNGHKJ/9frUYzJMPhYc8lq9j08voY3E1lGTtFbnU28Igt0jXoABU2aBRxXmqy0PvErKyFooopjMrXdFtfEGkT6ddA+XKOGHVSOQR7g18+634cu/DuptY3idyYpB0kX1H9fQ19LVj6/4fsvENg1tdxjcOY5APmjb1B/zmuTFYf2sbrdHt5NnE8BPllrB7/5nz/BgbV/PNdJpv8Oaq6t4fvtAu/JvIuCfkmUfI30Pb6HmnWbFO+c18li4SV4yVmfa1asK9Pnpu6Z1FvAr85ya07WERbWz34rKsZ0ZPpW/Yoku3PTtmvnq7cb3PnMVJxvfY04XBj464pkrVL8sa7QMCo2Aas8HFKXM9j5bHS5laJUkJ7VA7mrbxVA0R9K+xwVeikrnyOLo1buxX3Uoc1L5Ao+zGvUWJw1rM876tiL3RGHNOEtOMBqNoitClhqmiYOOIp6tC+caaZ6iZDTCDXoUsFQlZnFUxdeOhOZjSedUJFNNdEcBReyOeWMqrqTtKaiaaonfavWqEt7tWvQw2WRa2OSrjKk3aJpefQ1xjvXNT6ttkb5sVRn1s7eH7etenHJ4Sa0KhHEyWj3OmudRSIdcmud1TxBtXaCPp6VgXetsd3P61zGo6sW/j717mCymMWtD0cLlcqkk56m3fa4Nv3++etc7c66+5sGsS5vHkbJJxXpXw6+Et9r9xBq2uRNbaSDuWF8rJcemB2XPUnkjgDvXpYmvhcBT5qm59ThsshFWsa/wt8EXWvXK67rMLpp8TBraJ14uDzyQf4Rx9T7V70Bio4IY4IY4okVI0UKiqMBQOgAqWvzvMMdUxlZ1J7dF2PapUo01aKFoooriNQooooAKKKKAKOp6Xa6tZNa3cW+Nu2eQfUe9eXaz4Mu9DaS4iJuLMHIdR8yD/aH9RXr2RSEBuD0rjxeDp4iNno+534LMa2EdoO8ex4xYyKu3cTmujsroI3y5Nb2qeDLG8ZpbUfZpuvy/dP4VzM+kX1g2JY2RQcBgcg/jXxWZZXVo3clddz3FjKGLW9n2Z0kd7E67XOD2qUoG+YciuYjkZe+TWpa3bqvB/PvXg8sqTvFnnYnL4yV0aBBpNtEVyH69fpVgoD0rWOMs7NWPHqYBxK/lCjy9tSEbajZ66qdSpN6O6OKcKcFqtRjVGy09nqJpQteth6dRtcqPMrzp68zGMoqBgKe84qs01fT4LDV3a6PnsZiKK2YjGoX96ZNdIi9ayLzVtu5VIx3NfWYTBTbTaPAqSdV2gizeXkaLjdXN3mqbOlULzUt7tl/1rAvr0tu5r6jC4La56WDy/a5bvdTz35z2rJn1Pb1aqMtyW71lTzbm2e+M17lLDRitT6TD4KK0sX7jUQ3eqEcV1qV5HbWsDz3ErbUjjUlmJ7AV2/hH4Ta94oEd1Ov9n6e4ys0q5Z+n3V6854J44r3rwt4B0Dwim7TbTN0ww91Md0h9cHsPYYFeNmWf4fCxdOj70j2aGFUTzfwH8EXtbqPVPFJjZk2vFYxtuAbg/vGHBx6DI969wxxS0lfB4rFVcTPnqu7O6KSVkLRRRXOMKKKKACiiigAooooAKKKKACo3jR12uoYHggjrUlFJxTVmBhXPhu0lO6EtEf7o5X8qzZdDurds43r2KV11FeNisiwuIu0uV+X+R1QxlWCte6OVitnRuU6etWshV21uSQxyj51BqpLpcT/dZkJ9DXzlbhSup3hNNfcKpipSWi1Ml2FQOwrRl0ab/lnKp+oqk+kX46Kje4auzC5FWp/EeDjKlbpC5Tkf3qrI1WptM1JFz9mY/Qg/1rPmstT/AIbGdj7LX1mX5ao2cmkfJ46WJk7Rpy+5jJJcd6y7i927vmp8+ma/O21dMn5OBnA/rVZ/CPiec7P7OC5H3nmTA/Ik/pX1VCGHpJJzX3o8+nlWLqu8oP7jHvNSPzZeua1DVfvDf9cGuxHwt8UXn+tnsbZSSDukZiPwAx+tWbL4FhpFfVNedxkFo7aELnnkbiSf0FerTzDL6KvKd/TU+iweSTVnJWPIrnVgn8ROfQ1FZ22q65cLDpdjc3Tk7cRISAfc9B+JFfRWnfB/wdpzMW057tiBzdSl8fQDArt4LaC1j2QQRxL/AHUUKKxrcU0oK2Hp6+Z71LAQhufPegfA/wAQalGs2sXcemRHnywPMl/EAgD8z9K9Y8NfDTw14X2yW1l9ouhz9pucO4+nGB+AFdjRXz2MznGYvSpKy7LRHbGlGOyACloorzDQKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAENJRRQiXuFOoooGgooooGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/2Q=="},{"partUri":"/media/image3.JPEG","contentType":"image/JPEG","content":"data:image/JPEG;base64,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"},{"partUri":"/media/image4.JPEG","contentType":"image/JPEG","content":"data:image/JPEG;base64,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This Challenge is to correct a 2-D Interferometer data set for drop-outs.Interferometer data is notorious for drop-outs which result in values of zero.Examples of Pre and Post WFE drop-out correction:<img src="https://sites.google.com/site/razapor/matlab_cody/Inter_2D_Drop_250.jpg"><img src="https://sites.google.com/site/razapor/matlab_cody/Inter_2D_Fix_250.jpg">surf layed flat<img src="https://sites.google.com/site/razapor/matlab_cody/Inter_3D_Drop_250.jpg"><img src="https://sites.google.com/site/razapor/matlab_cody/Inter_3D_Fix_250.jpg">3-D surf<img src="https://sites.google.com/site/razapor/matlab_cody/Challenge_WF_500.jpg">Challenge WFE :Pre Drop-Out insertionInput:2-D square array representing surface z-values(NaNs in WFE array)Corrupted data points(Drop-Outs) are represented by having the value 0.000Output:same size 2-D square array as input with Drop-Outs corrected, approximately.Hint:TriScatteredInterp allows for interpolation onto a grid given a non-grid set of (x,y,z) points.Follow-Up: Re-sampling to a grid, Convolving with an Optical Transfer Function and finally Calculating MTF

This Challenge is to correct a 2-D Interferometer data set for drop-outs.

Interferometer data is notorious for drop-outs which result in values of zero.

Examples of Pre and Post WFE drop-out correction:

<<https://sites.google.com/site/razapor/matlab_cody/Inter_2D_Drop_250.jpg>>

<<https://sites.google.com/site/razapor/matlab_cody/Inter_2D_Fix_250.jpg>>

*surf layed flat*

<<https://sites.google.com/site/razapor/matlab_cody/Inter_3D_Drop_250.jpg>>

<<https://sites.google.com/site/razapor/matlab_cody/Inter_3D_Fix_250.jpg>>

*3-D surf*

<<https://sites.google.com/site/razapor/matlab_cody/Challenge_WF_500.jpg>>

*Challenge WFE :Pre Drop-Out insertion*

*Input:*

2-D square array representing surface z-values(NaNs in WFE array)

Corrupted data points(Drop-Outs) are represented by having the value 0.000

*Output:*

same size 2-D square array as input with Drop-Outs corrected, approximately.

*Hint:*

TriScatteredInterp allows for interpolation onto a grid given a non-grid set of (x,y,z) points.

*Follow-Up:* Re-sampling to a grid, Convolving with an Optical Transfer Function and finally Calculating MTF

Solve

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Last Solution submitted on Jan 30, 2023

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3 Comments

3 Comments

George on 26 Oct 2012

Why is so little information provided? This could be interesting, but is only obscure. What are MTF and WFE?

Jean-Marie Sainthillier on 9 Jun 2014

Error downloading URL.

Rafael S.T. Vieira on 13 Sep 2020

I've posted the solution as a comment after some hacking since the url is not working...currently the function used is the scatteredInterpolant.

George, WFE means WaveFront Error and MTF means Modulation Transfer Function.

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Problem 909. Image Processing 003: Interferometer Data Interpolation

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Problem 909. Image Processing 003: Interferometer Data Interpolation

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