version 6 of Startools used and reprocessing of Crescent nebula in HA

June 5, 2020

Startools version 6 downloaded this morning and it is indeed a much improved version, but i`ll let you be the judge of that, have reprocessed my data on the Crescent Nebula in HA with no calibration files added.

June 5, 2020

Looks good rob I will have a better look once I put my glasses on lol can you post the processing log txt file it’s in the Startools folder it will show your workflow 👍

June 5, 2020

will do matey once I get back in the obsy

June 5, 2020

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StarTools 1.6.394RC
Fri Jun 05 11:45:49 2020
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StarTools 1.6.394RC
Fri Jun 05 11:46:32 2020
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StarTools 1.6.394RC
Fri Jun 05 11:47:04 2020
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StarTools 1.6.394RC
Fri Jun 05 11:48:15 2020
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File loaded [C:\Users\User\Desktop\new cresent nebula 3 june\version 3.FTS].
Image size is 2749 x 2199
---
Type of Data: Linear
--- Auto Develop
Parameter [Ignore Fine Detail <] set to [Off]
Parameter [Outside RoI Influence] set to [15 %]
Parameter [RoI X1] set to [1026 pixels]
Parameter [RoI Y1] set to [1055 pixels]
Parameter [RoI X2] set to [1462 pixels (-1287)]
Parameter [RoI Y2] set to [1439 pixels (-760)]
Parameter [Detector Gamma] set to [1.00]
Parameter [Shadow Linearity] set to [50 %]
--- Crop
Parameter [X1] set to [145 pixels]
Parameter [Y1] set to [140 pixels]
Parameter [X2] set to [2719 pixels (-30)]
Parameter [Y2] set to [2136 pixels (-63)]
Image size is 2574 x 1996
--- Wipe
Parameter [Mode] set to [Correct Color & Brightness]
Parameter [Precision] set to [256 x 256 pixels]
Parameter [Dark Anomaly Filter] set to [Off]
Parameter [Drop Off Point] set to [100 %]
Parameter [Corner Aggressiveness] set to [100 %]
Parameter [Aggressiveness] set to [75 %]
--- Develop
Parameter [White Calibration] set to [Use Stars]
Parameter [Gamma] set to [1.00]
Parameter [Skyglow] set to [0 %]
Parameter [Digital Development] set to [90.28 %]
Parameter [Blue Luminance Contrib.] set to [100 %]
Parameter [Green Luminance Contrib.] set to [100 %]
Parameter [Red Luminance Contrib.] set to [100 %]
Parameter [Dark Anomaly Headroom] set to [5 %]
Parameter [Dark Anomaly Filter] set to [Off]
--- Bin
Parameter [Scale] set to [(scale/noise reduction 50.00%)/(400.00%)/(+2.00 bits)]
Image size is 1287 x 998
--- Contrast
Parameter [Expose Dark Areas] set to [No]
Parameter [Compensate Gamma] set to [No]
Parameter [Precision] set to [256 x 256 pixels]
Parameter [Dark Anomaly Filter] set to [3 pixels]
Parameter [Aggressiveness] set to [50 %]
Parameter [Dark Anomaly Headroom] set to [50 %]
--- HDR
Parameter [Small Detail Precision] set to [Max]
Parameter [Channels] set to [Brightness Only]
Parameter [Algorithm] set to [Reveal DSO Core]
Parameter [Dark/Bright Response] set to [Full]
Parameter [Detail Size Range] set to [1000 pixels]
Parameter [Strength] set to [1.2]
--- Wavelet Sharpen
Parameter [Structure Size] set to [Large]
Mask used (BASE64 PNG encoded)

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--- SNR-aware Wavelet Sharpening
Parameter [Scale 1] set to [100 %]
Parameter [Scale 2] set to [100 %]
Parameter [Scale 3] set to [100 %]
Parameter [Scale 4] set to [100 %]
Parameter [Scale 5] set to [100 %]
Parameter [Mask Fuzz] set to [4 pixels]
Parameter [Amount] set to [100 %]
Parameter [High SNR Size Bias] set to [85 %]
Parameter [Low SNR Size Bias] set to [0 %]
Parameter [Dark/Light Enhance] set to [50% / 50%]
Mask used (BASE64 PNG encoded)

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

--- Deconvolution
Parameter [Image Type] set to [Deep Space]
Parameter [Secondary PSF] set to [Off (Primary Only)]
Parameter [Primary PSF] set to [Gaussian (Fast)]
Parameter [Tracking Propagation] set to [Post-decon (Fast)]
Parameter [Primary Radius] set to [1.5 pixels]
Parameter [Iterations] set to [6]
Parameter [Regularization] set to [1.00 (optimal noise and detail)]
Parameter [Mask Fuzz] set to [8.0 pixels]
Parameter [Enhanced Deringing] set to [50 %]
Parameter [Bright Response] set to [Full]
Mask used (BASE64 PNG encoded)

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cjnLzxayNIL4xxGC2nFBFKpFRa5u1CTkqmxz/EgrBLsQua8LEZqTWAiXCYfqGqEI/UGF4CZMkzz4ybH8UxgvtJ1l5fZHCvPPCy0ly1s4ElehZUR22U3YRQn1aedSUtiF01+chG+L8Gynlwh/chQacrdwKLXCocKGmrJV2DgK2UdThNJj3SlUBhyUzxkkRKHshl3Yl5iQje0iJO8rhN32GfvCKFzOsl346nRCegrZJI30mFm4xy5Ul4YXLrOSF0QIaZ1eyIcTyqG6e0Vh5yNEklKoRi8UC3AKISQWiuktrcpJ2Nkt4UJb5rO8ZyzihkKEmnihmbALEvahq7MJJ6cJnYUazg3C+ZbPC0mc8HzNCKGyVqFC8+DBwqHCPs4hxAdiwneo0C1phGAXBgcXshDL2//gQqcvbxNCgNDecDsPYWy+CyE8SDhUNwubC4S08hBa8zkheAs1U2uFAmcsEWG6cMIyRFg8TwhtgDBBWwkWZruQK/o/llA4qZbK4yLsNpNZqCaNUAxBheV3ItyhrZMQz/XC7hCWDxXCLvTP7JmKlMJu+w+SCiE4eiE8SCiL1N/P/B4hHMkVQlp/B0KSQDgnlZAdse44aemEAK+EQrhHOLoPhgnfijDwLMM0j5Kpwtp/JEUI8FMRJBwqRVhIlW3M1nuaKOEyeDIhKMLSIrR2HlRI0gi/wFXId42bhGM5lNs+jBFCMuH+vpyQzsLsk0J9BOGU2Q6zLDx/R7t8rBAk4brQna9wzAeb0CsdJpy/8lqA9huotTzeKZw9i5DwwvZTwrbAhXla4fwVUgszWejUYFBhywtpnHCRLMKKO3WcUL8z2ouEEyaEdj0qOuHLIsQWQxIOZaSQrkflCP2UUJtdmOv+bhOiSS7ss+xLfzmFEOKEa9hhTigEqR5GCyHDOvMreMiQemhIiwl9asWlwvmoVH3lJxzLO4Wvxwr3HTwLy48Ltcdpb1ouQppUOHeFvnYULhXxd1wIjxEWZt8q7LEDn07YIEL4sLDRC+fPdAhfGfEWDlVSYa4VLntJL+x0w/oKJ4KuOhUurMJzNTnh+mP5vm2WxwsbRZjbjl9qoZhLhFzoLvy/REIaIQSTkKxCGiBUBnQSojuzUQdkQYT+oWOuEf5auArhBmEpC4HAhAtt1kO4lhmafV0hhBgh4ELqJWsUYV8mFW7Dhwvf+z/chDrrR4WGXC/sIoV+cReOx2f7oBAihWuchCO+S1VhFips9PeFCAdDj/QXdpbG+AQhBAvbm4QTeaZwerxwj6dQKM+t31SNRSgWEd7wLKHGGS6Ea4SmTXKt8FWFCw3qJwo7kxAJdYEPFuHuvETYphPqv+izhFJociFa1tZZ2jAhALlBuOb1eGGWQAgBQjYK2yyicBJb9nQKG2WU3CxEWsvk8cq9k/Br+5NBqCteaYSaRdg4O3QMFoJJ6NooHYTDw4Tzg5Kwv0zY65Byk3YQzgU7l7amJjphh93sI5w0wv3SKdxXorE50wkPqFHI/t26ChuzkPsO21nWCZuNRo9rTkI29lCZhcqh68bCW/jmhC/jdKdwbnolL0QPobdQl/f200c4t5TaWdh4C6WOGyA8m55FOOVE3osXCzfL0lKAp6UW/k2oZC+u2NiFbITlKDeyO0DI34oLe3/h3PQKoa6jwkIVltp3LhXh6UohVEfihIAKz4/sLARnYekk1KhchOuJ9BNuK7F/zH4R1tcK+QmIXZgpwipAuP5wEs5TccKJaJ4wC+neUkxC7tDyQj7nfETuKQP/IvFCqlu8TUgChfmtQnEpt3T8kNFCKl4MEeaphY0yI3exDROKMQs1gNRCuv2UhEOhFYKj0HiDQagLlX63CwvLiPndwi070S78EnHGRX5hwhwXbgMOxbFqTxNCaqExWZbVNiF8Ujhl2Rf/O9vzxEk4k/5eJxRqC12IkDtmkvAcFHmUGFdOLxyZsFGFavpaFu5/8hbCNUL6p1Ae7N/L/4Ysy3kh+An1SSVsa61QqiCmETaPXHQ44bzGbxNKeJL/IOu7vComlF6sPx9qUOHIVfmRJBLyWVFrL+kzuXb11Tr98j9u8Debbv/Cg4NwWRJV2GHCHBHSXUhEYTMWgnBblfFOISv8dG/LiYXzGN+HUCHJv4JeSE9hm2WVKBxQYd4hwuX/LSaEICHwwloUsjS7xS5kcRWCr/CVZU7Pv5nwOMuVXRgToxCvX/n5hW4RwipkbTm9MDKCMHcW4rtcSrwQDuGkND1zlzKEXCCcxxwDhU28cCKuwsIOIZ8Szjtw2FuKSTi5C6mLMFdH1R/9WThvw6ykHxOedR8Rfq3CKpEQ/IW9TZitwjpECIHCvjqEy28W4SurbUJIIeSqO3USHg+0TAhWoXcsQuCFanc6/0SzahWmDxO+bcIpswj7rNK05XTCVwJhOf8XL6QeQvE2Il7dH5GEebSwtQjRYMIj/dz27hROXH9t6C4U8hJXeVyF5I8s7MQ6pwjVrMK+FoXFZUKIFk6JhJBO2KnC4UahQ9YizwvLjwo7J+FoEA5OeO6kHZ9xMrTSvRfq9i93kWXuetNjhetGzeblfLZwKaltgc6IC4fyImGnEfbPEO6j/EcUal4iSnhS9ULDwd5H+Vcq4V9rfJFEIa/i/72WFCLOOpakr/hqYxCO5Xwb/soXCjNZqG8+TDgnRwj9dcJ6uTRUTxVmP89dZL5E7xA2qkoZQTplq7A4hWPZ7luRuAhJkBC83nH4OSO8kNaYcE/O6Yh5Smeh6e1W4ZxDWNmEgAnbi4S/ZGQ5JWzzzcJyazO4kLLhyPwIsRYqu/BtEcLIhNsvJc0qg/ArTgjxwnn6rDLMIgvhDuG0C7tTSFyEbGP0/6gTCNkdxFE4z0imIkAILsKzVhFXIbCzHCBkMX6gxiBUKj8qbCUhCEJ+j25CMYJwkDZxGmGfC8Kv5wlH4dZ2RTDhWMhziqOOyl6IE+q31nwh59+gzUXBYCg+IzELx1IQFmZhh87jI+wrPyEoQggQskZxCIkoFEMtQjFmIeiFutqQncImpbBzEh6iTjfFJqwP4Rta05vS3z4jfLEdwYS0dBdO5u8mbiys63FCYhECE9buQkvSCdvz2Tf8Fz4lBJPw+Fpv85zLAp8LTj20uJALWV5qK2gDv9toljnOQo/qeZ1wiyzM3SeShMaT5SkETNhnWWasG3ynUITFTcIs06zF20HY3iEcViHxFoJVyBdMT+GoCr9ki14I0psYhaOvEBtnFa69r7cJlTZyo3BdG72QSwph71r2j5WZRGHjJTRWm1hhaRPCU4SwANkRXZqkn1AN0mpkYesrPPt34/iGscLCQ7iclfrYlxcK2YcaAoSwCs/fJ9dGcYxVeQhZWt854EcT9ue+lYREvXoKuYGdhbleOJqKSBph9WGhMsAibDjh4C8Uco3Qf4MnE3JJKGTvHSi0JkLYOgnDJUahU16iUI3fl3Be7E8L7Q/9iEKKDnVM1qGD5JKwSSKkdTLhJJ+vpMLWJjzzIeGa/Foh1pfdhcsMdB+0SSEUf+3nQ5hVsUJIICTI9RTCsXAQmjMSg3ComPCc1L3aJBMOBuEv3zZh6ywkbsJlixNHYZ9fLdQt4LKBgoXcCR6/bbswSLju/zRCuEb4Xp4KEFIfIevWMcI9FwnPh3hhZzauGUoH4fTlJBzkV9EJz7gKC0movYkJ5VYrC0ekFb8/I5x/cWjkiYSdSbhscUQItwkhkXCofIXgKjTl40JrVmEeLDT0+FlIkgndRrIKxRumbMpNw6G1SqMZfYVzld3Gv0noHVm40f6wQuqpI4IQvi+hmFlInFWmBAqFIEL1b6qQ1tcIK4cbI4VjESOUk3wN534x2rqhVwKEXZyQphLiTfGxwqPdRwnH4gJhhwj1mcyb0F+onfVu4fittOLSCS1Zd4D8FqmFwSFphdINzpl0VZcTlhcI/TLmRiEbfxE2AcJxv+9K4XJ5KuaP3YQMntX3CNftGCnsywuE4CLs5T7QeApH24kOEvbVPcK1DY7EUQgBwr3T9v/0Eh5j6hu1FO0L7ML5rF4irKxC26kwC0cFEyUcqjihbk6dcPmh7SWTVfhrESCs0wk14k1I2TyHsFGF9kLgKNwqtrdwjSDc18ZBOB1d2yqc5/QTbjEJwVHotrKOQnKfULMqeF9+H3fliHB+uLYItd8yTKgewS9ceIRjzf9UbiAbtBfLUkJhJwg1ERcuXNhjDcAmfF8jtI0jIZ8p5NqVIBT29SEE+aNQZMJrhaAIG+44BwqxuhwjFCfxFCaMq7C7Svgl/4EECuFKYRMi1G4ldSXDhMQg1NXwfW8ahEOpCkm4kKtZ3dXCsCQS8nEUyn/Cih8vbGRhYRG2dwvla1bhyyiEdMJtSKSK6WMQ7v3gIOh2i7sQe+kIoUK4X7h86Rgh2yk+x9sopHrh785CigjhcmFzp7AKFIKbUPe8V8xnWJlgKhyF4C48F1l7qryFy/+9hcYPxVjNfOvoLwS9cLpAmHsISUIhXCNs7ELtxVEqC0sZJtFC8BNuTk7YPkMoHcC0QpYc9i/kIlROfZzQc9PeI1wf0Att1WMUFhJZfVS4fQZEiL3SIpxyg/CNC/sHCd/uwrGYsDOlEcJHhOjEYUJ4hNC2/z2EZYxwny5GiOVtuyGVUC6KzvEUNimEfItwbWhnmpjLcULH3hYrRG9wFB5bSTn1jxESrZAKBFqnEaKn9FphZxMSu5CPp5BdNQnhZRUeQzsJ5ePBhAQRTv5CcYLlLHsK9WuqlKBTuOznK4RD5eXEiuQmNMZD2FiEhrkuFeoLmSBsLxWCWTg/n0KIpwnu76dQm3TCGJ8oHPEu+yRhZxIuUU5Qk1IoF1tZSG8V6qqFTnj+Wy/sRCFZx+UHd10jeomwUcgRwiJauL7mVcK+NgjPnuggBJsQff0kQjWiEG4VcisTJQSdUJ/OKBwdhCSBkJ/TT8h+ap2LcK16/kJIJtzPgI9wneEZwvWCRvhb/XkhuUZIECE4CAdVyAY6u+9RFGOFmnQW4VykpDpFdK+6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XkQosvNwilB8HiFeY6Zlo4TbY1nNA8eE7FojWee2dYV4MiwhNMl6cKAaniK8ZApvJwrJpbP3IUMItLCPTIUjBJYwMwVxYU03NH69uPQN7QitqJ32h4QiRyjYwuKgEN5LiLyV5HoQEa+vBfVxYwvrhwpjoSrysDhC8oR8nPCKCeeNpNx600NQgOhTh+zFA0LgCSEUxmIRNrFPQhG6KJuwttYxIeRQ9whbVNiGQn3qSQc/mTnCcZnjG9sXmgI4g7qUsOWqqRiFKn70fOHL9kKMS9bFCFO6TxCmvogI70lhvUOYdfIc4RStL4SUEN5fWHhCpE/996OEr1Ehp89B7IBlxPNnFw7fRYTMLj69cAu5k1rvEspDwm6PkDgzKaGTlv40oaNxhdIf8Pmg0MKoncIp7rQQcCHsFcY2lXyEMB5jbx0MzjJkCvU9fN9eni6U3laOCIUMv9NCVSXPZrNTaB+2e/xrIxxdtLA5U7jEm/5xi35Wmz1thNaTBwqve4RztM6RWX838dEsYcPRWXNPCsNH7y70ot8ljN+BZwrr1BVEtmMLD8YfSXg92pX6j1OEwUW2xWcS4rvyym0/J734SGHs4MeEzmV1bB9GCSnhl4oUAkt4eDelJqCq5OGvTxDKikUtc/zmxclCcVR4XW8NXzi8TL87svcmWlSNcDDC2h9ekZUpLoQsocSzwxNW5wnvmUKrp4hwjRZ2hN6HPSnUIVMvRHIwLRz5A2vdbSG4QqSDiLBmDqaFsswU9iwhHBduLcp+G6DIFerH5TJfTrn7GKFeEVSoyiHcc0WG0L5NvHbWXhJGiB6PiFAiwrbISyNeI2tC2ESEwBRe31+4jIs+lUjfTGGDCuvghxV7hHPfXklOC+cGCSEgqfXrIUdoBsyJqUFjCYnUnS3s+fpZCKgQrAsxU0jFNhJXOGPapd1WH95PuPxlXUOh8GIp/Ap2YQgbTCE4wjmurrC1tSv2l5IWfnWEQVpn4TVXCAmhScTK+Wplxq+xX4luOcLBzTh1fDFh7Qr/p6Qn2LGF9S6h8KC4UJLCIWI3QsCEsFeIdaYOCSVXiIYWqtLdK320lZksT6hXmX84MGGzS4hfxrgQdgm3Mb1cRIXq745wm5vEhUALZe7ly52O3W8gXP4sbGH7cGFv/d8XWheAnJccES74IuyYEgarUSeFqpj/j3Zo7eAa7keF8xgnC03nYx19W39/UuG4z968Z1bF5o2HC4ElJGM6qs1ZwqCKICWq9SZVcoXweOGVEAJLOPd9p4TxO8AMu1fIia3voTggBIYQHisUESFyzXdVUpgf+4Tw0cJtJFI4LpYllHY7b4tgO4YuUOu57CitFt4q5/aJCHunQ0zYsYX9MWETCOf038bfBSJszxMOAhe2ceH8+SbU7Uxf11VR3Kl0eHEhKwgp9L77vYoIi4gQ2EI4JkSuefknMS/H6UK6gqC3XijUHvnzP8/3Uu13eemXCTbm22TUjxQG8bzuKC2cuJJL9YRu1Y0JrdiEwyi8iahwnIGYZ4EIo/Vs2WV3VCj40+0IobVoWnj7GCG4J003vBih/PMqbDGhrJLCbZx55Yan/hQhGGH3Z2dEL2RYM9wwwv+zhMEnfZIp7X/0Np3+lV0Z5CVHCIHw7ZAQMoQ1JlRVmxI2+4U3LfWFy16fqlUg9EOViZQa4dhrkRQq55hNQucOrh8qVIJxGySEyzfVJBz/bx/bntcjKgRcqNCqwBFu7e2t+g7CZYnUl9IbVkaFShcmUiiIxojQreTrR+YhJkyHsgAv3mwtoWAJ/Y9KTJheFFKonk8WDuU9TGyOsN4lDM97QhgmZRX21v8jwjmWrSqft2O1vmqjraa4OsKL//E9lqF9wm/O8XuA0NoeaSEgwq+VvaIt0b7hCkMWXiOZoQIhJIWNI6x3CwWJElGhTArBEUJaiETPFk718KvTl2QtSVoYj1EoCaH0hOCflAPC1zxhxRbe3A/eRUgNND6UMIj8hucJTfeU0A3/Mhq4l1MobJcyxBfyXv/uif673K6YHULYKSwi36/CrTL80wnC+3FhSwvH2hMTLi0lKtzibf1Xl3L30lGZQr/ZXmHNFiZiOQiu0El7ojdKOLycK3Qz7QjDC0+Gp1sLzcFXttDfh3uEXckXdnj9sYTSE5KhWJU2NuofQsj8Vtr7UHr78LiwGEcVbCF+N/knBa25LT8PbrxkCXE2Vg+l/0BfgkPGkq1CVfGFRHXEhF621XO2cM36i1tgjwpV1Z4m1Fk3QsbePSBEYD1HOKm4woYSSkQYxts+odwvNMvlCINmuvu34F1CCJuQ9eEihJQQiUXYjMLcU4wI74wWPdwqT9jJpzl7TUR4TQvx5mq/0HrUcYRthpCof3VMWOwWztFe3azEhfitlKiLRexlR+xD06N9whdhmKbeEaqfy+CE7BcOjxL+P3V1syu7jRwA+BhnML0xLC+9MC4D+AXinReGG0EWeZC8wGTnxcCaixOMvAiGr3RPOhi9Rjf4AtKOC4GMJOqHP1XForr73pmagW8fiSI//lWVCJNett7Oi3B5dWH2mArfg/WAhH0yW/fIiOBBVC3KhdiNuaRTPybs6uBzSmjvEQLBEVpcaB8tzGcfC5ebsfL10RaQNSWJKyJUR4Q0XAZC/FpEeYCsC96GLcLmn0IYtOCX9zXzDzVHuHfcM4X20UI8ZGHHmWELhIIptA8TYiU9M/LjhdDFaO4QlmabI0LxTyD8K74H3UOFcUFpuebjwln5kkuqTbFQACtxl9DQwlOp0ERpHgkFZVBgZjzhJbMUQQw84VifBo7ww4v9W174nhlNP1co6Z6aVUjkC1+oMsKeLXQBCeMDlxd+N//XHUyFnkO3Jv1yrNTyYDs8MmypPKHKClVG6Nai2dtGI0bCYApa0MIp2+SEzbSEPVdoM0L7PGEcyRNYqLlCJMwLXTCnUf/733jCNKa5d8u/3pji0cJhEqabfFgYxijs7xb+OyiEc7gsFebmOp1DKvbSdlSogUHPtFCVCMnICpnfcYWGk+HBNSEC7LPLVHhAqChhjwm7w0LqZRyEsA/+1evMPq+w8oXY2kVCWyTM809ZoV9hEuG2rkrnUkZOcp8Q7WQXAi9bFm+o6St4yly+UfjqrHcJ9XGhzArthRDaV+LbxwjtnULm4StLkkWxCtNlwYRT9dsSD13UHyW0sZAs5DPqBgoPLWSfe5EK5T+CcEwwiNDmhVPcQFIqbL3nLUc4nB8k5MYlL7Slwo539PsnC71wwv04zcLtAqNXNSdUvnDuqeLN6RbgxEHh+JQp3EN/bqH9zEKz7z5wDsUBYXKt9ZknDDbHfLUKB0GhBKtrTQs5xUfFX9wt9MtltPiHkvcl2M21MjCElhYunXYlFVoyhWoVGuYhyQuZPTWEcOrCeEuU1hTemUmE2v/jmFAxhZIrhI6C+YnVuiG6fZBwXDFTIS/48/FSyw0XA0JGfEbh3184IL6QGwoWAtv315fDg4jI2JZ8fIn+3oXXxwmjVVQHhcG5cEL5fGFbIOwiaCw0dwg/8YR7jgSvkSecP+sfJRx1H9HiGApFgdCtrWAJ+7yQ41uE80LqzKxXoSGEEhK6X8MZEsoSYRQQ+PHCKmmdCOdEElTv5e9UqFyehIRNKrSg0G/wXiCMl23YhV6WXpsSwmzICLIJzVmf4ZuDCi0q9JvfK9yneu7O6N1GhZorLCoHoND8whJiYmnAb/X4P0pYMuB48rvzemFvpUJzGiqsAUfobcN44FRS9SOh3ITd44SWEPbHhdeauZS4kFP1AOHcW7Luuh6F4ys5JTWFCyUohI/UlSdMYhWmx9uclh+bsM0Kx6bmZBjC/KEhhU5iXgPh9NztwA0U3jYhMtJR4bZiGaF2wmkX3sEOViG6zLFwAMo7NxZhcmjywjR6WGjvE7owL8HlcDtcLvQvNixsS7LuHUIkeoEJp3zW3yM0HyBheaDCdcJsoYmOhRHXuw9KMkYqLJh4KuxQ4aFNkWDGvkNo21Goa1pY5To5IBRs8qdff3iKMBSkdUqonGwtq5/+hAr7I0J7RAglzd4X5qLPtigRdq50gUIVCafvuj+9PEVoc0LLFfYDS6hLhd+hr0NhuwnbUDg/nJ7p6xcQetGsGNmCqzoO/ekFy1X+J5twPl9j8iBuS0aoE+GshIXjcPrlp0h4eYjwpHlJehdaQGim4TyhxIWWFraHhasyyunGE3795yKhH+pZwu2o/m0SVnv/Mhg1H2vbayjUiJC8baBQfjMJpXTnwBsVq/MSFdpEWKWfM/PBuMGbcO5lEsq88AII17iGh3IWLmfogFDvnTmhfTn7wmnPPaFgCcO6ajwhmA+ilAgIl91xA5pvRTh0cKZSIZRmNuH8EhNaptBGt2ESbtGjwr39LrwkQu0J0+nyhDYRvkRCEAM+fMsL8WXih3k5+cJbTujFYWFZmK//w/+TJbxhwmXOjxVaXBjPZb77am90QdoVCOMCAe2/mISC01sitHcIXWcsYfX1r/XDhD1T2MfCNQWDwkoXCcnSnRWqSGgjIRTSauJtIBSYcOs/K7xxhf5iXsuy0yysEKFFhX0gxOcICPW1LtAty5gI/RGYQiyXCU/YPkoYrkEknErcUPdJV7TQxSUjBPo4LLTHhcTVdH0EPXEuPVPICa4w6V+whHwW2p/rgkhvGhemw1fHhOPwQ0b4lhECQ8VCdUDY8YUSfjltMZEpGUI6rp6wPizkxj1CTsgyoexh4RqiXJhbDYmuIynEvmEKe1yYgGNh+xhhz590TmhRIbm06NKXCDV0ST7Ewhb519om/jbKnVHx0weEyXINZ5PU1MuS09Pd3YRdqZBbT/QJF269X/6hhMOfR6H7ehcqqs72gTAK/ByOY4DpXm9dLnGKBp8PZiy0B4TQfVeRsL5TiKoEIFSAUFFCi1xTnhCLBhJ6g/tnaxTeYhtXOEYXvJPeYniDiOiiDzUtbOPTHwn3z1Yh0E+7TAMXepEIzw2xvtMKx0IjIqEIhFBcPOH7MmyBsI1WORbG98N877dNhD1biIWzbHuxNMWF8ogwKrGX5d9pVF9oCOcsVLEbiFNGuPdDCP0O3v2uwmZ9LGzVvppOCMxJSDgLehMNhe73LyJqJvnCbX+1LwS/WIXADI8LieijTgGhJs7FJvSnsX88Vn7/Lk8XvssIsa3xhOm7UuGeDiOhjYWWL3RxAT/T2Kd7DoyEFSYcH3TI2G0qHK/2V860laQLsb6oUBDCJGMPZw02E9DaAUJyhdtk4RahqeKmlLCDmo1d8ITNfuQuGNTEpToRegbJF8Lx0b9N4/K/hcKOEHq3sEh42t9KhjAYSVvzehV54SkWhnGZP1uqUSL0N1N6fbyiwKHaUgNTuIyx9t6kQrsL53PYY0Lv8V5qG1C4vDfVNU76qNDvUPtXOSOc+03LvSccwrfjn7vQNtf4GiQRD7cIB5GOxRRGl/gv36d+rrCHhG0sjMK8pG9CYXQEP+1CoOY317jA+bkFEq5/JsJ1ryBhGK/+bJXVIuncy0ttV4PCBpuSJ9TJ8TkmBDoPSoAlhFMoRGieIbSPEO6xC4MSHefDNN7s4XB5I0zNuHDPTYVCMLpkZK7QxX5AxJ75EuH8LyqUh4TzCrW4cF3BfuPMwj4UBtJiYT93hwnfA6EihF0qtI8WQuv3Ln0hssZzcISOyT2Ha+Z1Qm9DRInQxXRrO9aorS/U67mhhUkEQhktvf4vTOjNMVjBKqj7obCmhWA9noQz6rr0H1t+3H5e2kDoReclF+OE623mCLfc5M2tCVrMwr/UiHD6Xk8b4Anfl66X5e8EKrSrUINCdVi47bU+T99/ioQfV83Ss9hBlNAm+T4jHN+IlfPh5Zyg3cbsQssTRuNPwuG8H9s2EUIRCu0uFLHQxSW6Zh/dSJvwHJS58ercHiq8esJXWBinn0nYqG1CQ+ibhdOO+UK0LyqWibtb/FL7wkyoRWhp4TLMi42WeA7BhzqhuynXmv0ZJbSREIqMsEmEtx4U4hm2Vcyp3C9cop+O/cAXhoMlH+4Cdl1mCZNj/GChPILdAhSSLELYlAq35ooWXsqFEpyXt0k8YcUQjq9UKmyTn3IX6lKhzQmHmivUuNAra7hwjjk9yIywSYVp9lqFJ9VaV68KhEQEwpYWjp1Nrvk8R0Kv5J5UkwjTqGjWfUK3kLhwLLi37LjcjLFeb1yoa1gYNYqEj4tImK5Pr4MUhQnXx9OOfPPjV88V6pwQuCObsJv6+tdT9Fo8QOqE7zmhqcbzUySUTxLWWLNE6DLj9sEktJ8sV4iWx54SUj3NwiCiT8bptanQFgtvR4S9E1pa2DStQsd9glDsPbUd2NV7tCGTEKqJPfi1KhVOH5gzILS00CsdTWPVKyJsc0LqSCzZ5hblkrzwNgu9mzUm988p5EQktPjV/VJC6wvNLwc+Z0VY9ZApQkt0ifr5mieUbJkoEt4gYZDeVyEmGARH2Hgd7kKojqhomfJC9Tst1AeEKhBC+xoLLSKcrqtajmQokInQfj6hF74wjLkStaRQ+je0CV6tQv8vC7TIhxPaBwihgG/K+rLCRmVF6wljhzoi9Mue+00Ke4YQj31/hl9rrnBAhHPVk6HQnJtJqMnOF6ERHSXMdJIRnvPC9UaPbW+wYhU2COGY0I9AuOztKLSBUN8htFyhoYTWE86o38f/9KOwm4Xmp10oIuEavpBf/GbhJd+sTYTT3kx/JEKJ5ZXGWwXJp5LCLhXaUDic3babn7Y1m4XYfkzP5fOFO3MUuoUzk1TtY4LCDhHeskKbF8Lf/lwP5/WwfUzWCutO3qK+Dwqnhe3cPRvgdKBG4X4dPpLDvPmoA0KI4LaeKRxbSLKOv0LCKIMdF4rktfL+O//rWijs3PeAcOyal3JQ4T6ztDh7RdWtYrcJBSEMYxUGTkCynMPT9qApEl6d8N26RF0mtLtwla3/NoTwFAinLwih+RdUGK7GGwT3hJop1IF1/QKrEeMczA/uV7sIoy/5oaPWqbAhhJYQjmlPeAdpuQtG0F+aKie0idBuwoY/cXdSxTqcDITLe8kThvMJ8r15qWiGyj1YhB0p7P4TFQJzKBO+Z8meMF3jzyfstm1IhOMQ+uLXCGCcUBjcqlQYVEFpXuCrqlEhEL2eKxAu7IjP80JLCxUuNCdMOB98J+xRIVqOY6HChW4MmRcuVTwUeo4OGgIQfucvyjoKIrShUIZCGQqTGH47E8I+I7SJUMGyQBi+kUEzdG5BnKjFI4UWF64Lpjbh4AuJAIR/qAPhAExPg13lhdY7ZcM5i8NGi4RdiXBKTy01Glt4CUcLatkfwrt2dVlOfhnhxY3WBsLoQ0hoC4TuMBNXIYrFcgmgodCWCD+cQ2EaxcIzsJTkvlzhWraoHi/UsTBZPnG/kNoSPLpAuPeRFY53WRQKNXfB9h0dfy21ZrqFbSL09wDgCPSN/6xcKDzhq1+vZ2GQMDKnhBDaZwmjlMY7x6uwh7nxObxmZhQIzasvSoVlkRW6+BQ0Yd2egX3jiWnTwiYrFFwh1FAR3/5f/XtugC8mdF17Qgn3mAiv4JbdLdzujIaF1gk7VFjBfCoaZMYeRe7rFwrlJvQOOiW0npBKEAoWBsEWJrEITbwNsZCKO4Qxpt1+B0PrA8J9uubMEfqDEeEJowUNhV2aDxlCVpLMBC4MwwkVT7jd8kcIvePwOOG7zQhNtmCCQjJ0XmhTITrmEWH+o9w5hIUaWoV0sJboTnGnRQij23hb/u0xYTI6IDxFbZpi4TKq8IVd+MUu/Lm+X2ifJHQx5ISWELrIZVVPqFOhCxkI/RFaTNgiwj4VRqHrdN6RMA1AWLnW6Pxbl6NaQLgmrxYX/l4utA8UziGsPCTULOEg8kKLCJcYKkI41E7YJcJcqfeFzMzsKyqfcUIqnfLyw5UtjPsaFl60CNP2UAU2EtonCg0s7Gqn6JnC4LZrSChSoc0JL/7zAmH8DBVar051R4TvV++vAuEt+nu8KbEQqH1s4dylW/n3T7gw6HNzSlQYnJ5VCBf6UXhjCKcLEgiBEPaVJfRebYpVCJSpAqH6lGu3CEUPMtZQ/ivvDF6OC+2SZK4M4TRicKheYaEFhDYtypZ3Du2eBvEGZ45wriWwUIS5oliYn8IkPLGEwDrNH6nPIKyihwnXF0Y3F92k+4V7CgSFYWDCYIfDRR6FVdLHQWHyRFPfFQinQq4wYRetydZwTRfmR4YZifFoaF+40LJC8QWEtkBoYWGLCs/HhBIXhrEILSbcl3bqjMiKdN5vCaHMClnhC3WNCWWxkPpoF8ZXDhQ8Qxj/ukPYYe+NCIaRlOlgHBH6n384u5WV+VXLh8bPYXghSaF3WUz1bOG0RZBwDyW5QjCyaScadxYuz4QvbLNCjQnRLw8J/We+UO4boiKGSu+ZQH4DQ4nPIbSsqBAhvcKoOhyVFtqHC83PJFql6xLWFFU+ZUJ4nv4vUqG+Q0iFZLZrw8ZfUKjmuywKhVMt1twh7xeemEIke5NJfaunoRDfySGpM5hwj9M61CHht3cL7SykmhQLW1ioz5hQ5IQVU4iEpoRirbTmpcMSlIGTiDeNjLAJhe6K6r3VU4Udti5EqLZIuLzVm1ByhHP3jxBKYvOjIcdzaDGh30dvgwXAhcQFZQjTMqm3uyzpcSLhPK3l4Cnsy8UAHRZUKBDhxRPO4+aEy18VKpyGCoUqZ0GebkJLC61/Lm+L8LdF2CJCSQjtnUJbIHQqDQzV+E88YYUKk6gK6nIq3J6whPaJwrXDimoUJ7esEI95R5etpISXbX1O6JT1uoJwrDc2vGBFQrEJL0m7j+VCwRRavtDSQnXZhZYnDO5ZXjh10AP1zwtS+O6ExKaI+K+HCXW8hmm0TkjHIpSw0GarLyYc6ki49BNXxXe4265GhJklXgeHobHwtzNDqLJCGwpV9DEuzKzt2MggQk4e/fZuYVss3OOY0CJCLN7Wmr0/QOOBQklwAWHzOCF6odsiYROCQuFr2rtGhF4M51TYCI7QHSFcqN4iMZRynyBce65JYZ++seDWFQgtJoyXLhLaAiERs/A9K5R4D1UqxCddz0LqPS4M0nqbW6iMEMsI0LY8QCg3YcURThEJt11/ohDHgJERmgx0iw4VWkQYbrbmrgZP2MGP/j4J6bu1sPrlp1bFwjgKhQ3J26NU2JboEuF85UW50AbCJKbjt8EAoSKF6WjzuTfFQiJEtcFM9ThhyxQusSTkhhA24+YY6N6Dq59LtUTVKxdaJ3zDhOAXMtPi4UJbKGxLhLf9Z5z8J5f0hUTc3qb/Cq7QFgjfOcKkg+Tk30pO9iiM+9UMocoKe0poy6I6JPTGDS5gyxBa1rtQ2BULvbhGwn5azN5frf1oxp37h9H8VPOE0dfITSGFp+gDX3jxZ/hwIRB9Wn0ZwrdlSIbQ4Hd+fiVBYRcI09iEQI3AhVb/BmgGULgf4FC4PFdsIRDKF4ZjDk8RHos3eExzQGhBIdMBri9Ww7DEY7K1BziHiu61PSSk4g18KjzhUOfnvSf+1lSQsAeEawftFxJu3YjUe0R4Q4UXTOiviqeKhe5vIHP6wp4Yoc0Kk7aA0OaENhCKON8w8gNc0iIhdCL+J1lhcVA4Lym0CO85oeQJNTJ3J5TxJFbhN+dN+AMpxLofb4rsKOGQE7o1rbC3f9zuG1sY5EjzIjwheek02QQWjrn1j2cjlv3FakFWuB+Z6catFzC54fcLsVCoUC9CzRK6d63N1ql4bFpIVqFZ6OdlVHjbhLZIuHyFVWWmkDNiKuzZwHDtelwIL7IvxKwtlKtKhTr/3ZOEuljYlgq936JP71Ui1OEN0nSKSoRWke3TVeIIw5iF+zXKC6PYhWC2mOvoGRHOW5cIowdVJLRsYRf39xDhWypMOmcL4w3cO+kiJi5MTgFHyLvWOhBG96ur7xFaSOhHzxQatjDk0sKL9xu7Q7BQk38fFpIXGRWCLfJ50mu0CeVDhZpskb51+xNs0yZcXxwSytwCMCdICS0s1AeEmhTaUqGNhdEadKEQuZKAUBbafGHUMdkVU2hTYWOPx3BOhOlNXieDC/eEfEuFF1y4nBI9INljqPtdWOWERvSh0GaE689EqI4Jt13HhfaoMBoBFrawMGbYXGKChS6ipbqB3VgsybTkzPcIinD4hhLOLyPh9RzevCQ7qKwQHOqcec8XmlMmzdAJGyNzhECaMaJ9sPA2CoNzwk2qlDCa+HOEAujkxhBuNH9ryDRYJZ/Gwmhk2fKFVLTI72KhjUdestbdQjymbqaqNG4XIXS/1ywzXUBdB0Iotk2d0vsq3HvV5cJLshbbBT0ktInwWt8vtMVCWy5UqNDbN+91u/5BCKPHgq9bu1mFdhNuIRNhGwuXMecPq7LBuZNywoVGCed9WoVtJBxDCFIYF2SFCjtQCIdMhmj9JXU9+kLNFeraPxf9YSE+hBP+fN77tdMSdrnPNSVcAhFO9+uWMcbnxAnXCIXImeII7bOE05koEFp+gMJogAoedVgfrD90TphcGL5wipwQWpM6pGbjsDAu9Vxh9whhTg0K18gdzVE49c8XctVMYbMI8VK1CO8LltAeE970KhxOR2wtukktLVxfN0ttWoW+dP69939M2BDCliOMq+dxoYQeqs8gVNzFAoXvsxA6hZO7tWIXNjasPK2fe8EYvxBZlpeyEGFLCQ0htKmwIf88Ihzq99mCCIcaFPbPEvqxDDLUt2W1vJCE0IZCiwuBgeUhIdBy78h7PQq3m9Gj2ZMWspaUWIas0AtGhUCEokSZ79J/6wvN94Rw0ycdjkJT2WdFKPRWouILLS5UB0TnVLg92+/P2ZxYwooppDeSL9zz+Nm8+q0k1l1uc2kh+HR92IBCe4fwwliY7RT1hNtyhGNwhYoWLk/dodnPeT+P+b9cIdFAI5dbpb8+1YTQJd1YSC57Cwv/v7p6x5kcNwIAPMYG7Uxw5sg8g7MNBtuhz+MLbMMYwHJgQFfaHw1Y1/jbvICUMRBU1oMSX1XFoqSeWRfsnb9FqvixxEf8v89lWCURmhIh5IVACcNP7oTLECYVAi+cg1wjbxeGpTUIOiOEC4Qu2lSRvenMMxL++3FQmFRbg3FQ8sxTDh/SX6EQUGH/exAGic0PEEKx0I+8UDHC6RaWX6TCMMVveMJO0H14hP96lbp2Pm6r/iAhfWzaDz8JW1rY52WMcMweP5twUjyR7x4JwQlBICRmzAqfR4TKXcm+0ESDjl8fWWESIuHACNEkoRBSYfCUWW5tLRNuiai2UTUXCA0uXP4RCcHe7Gm/OhD2TrgLNJNULEyXM2d1rdM1UXvX+PC4Vgjnhd0N1nnSwr+uQsmpgYZa3++TOT3Dn1Lh9NXx00blCxltcRMI487UzzgmYRyb8FkkVJGwvUx4zwpBJKxiYbOvzIkw/zkeWyeIsEmFLzaHIoQQCgehkOm2f3BeeENTVtFD4wuNmb709UKIhIoVBjGsVXNceI9QpcIKE4JMGK1D9ws9bvmrPxGuP6chDgr5UtHCldnEL1BCsMJaJExDnxOOVSoEXJiJnmrYIK9c7yZkFghlF98BoaGEQX0IoZGgZEJwwq5c2GLrcL6O5EItBQfCtQx22bZM+rDNCucD7A1C+E7C5L7XNfp5esHkRlTYMXetJyRRiRBSYcOVpcuwW7HQHe4XCfV1wjUiYU8K0zgnpJt0JAxP6WhdDQIoJSw/yWw8Z2GzT2O4FQi7koHOCatdmNzDnrFlhAapm/+m18M8tBSnMGErEy4Lsxv3/c8Lv/lX/CZshELYhXhwQhh/OiGsskDjhCAUurDCPyT9ayzJN1OlMxAIQSIkIhIOv94ToXdC/eaXePu6rHA59Q4J1/T7+vtghN62Mp6wbSSlc+fyOaF5wrqjI6F5REL4PsI+Fc7/+Qw6tU74QLNgt4dKhEEUCQETJoOmQv4I9ITYvMYvxGzjqfd4y+d0pu5b504Mo1mtctVLXtW80LVMwqdYmIaW1HP4+T1CiIRd7otozQixufnrsCKFwAhnvqveZ3bNaDQ9xNf+HrNwy3+DslC70MUR4ZpnFAsrKXDMCv2TIwVEwnjimLAuEtZjlCoNXkiREGEYtFAfFk7lYoQ4uCaFe0QXVBMLkzKxQq4ZjVsobKTCvmiUdwh7SjhUcbs/ihFcTFmhLhWGS9aW4bywJ1t08G8TCpH3R4UJCX9GOG8qXSjchqR3SplweePOClVOCMVCPk/aTAv/IhY2SVGY0wZ334+0zUIoFtqslLANZyIRAklI6q0vEi644Y8P7/VIqJYl6AAmW6SeF3rxxIV9kXA8KRy4IyAndAn+5QvDQIVUGGqAJJYytBIhmaa3/ex+z+x6Gx2Xerh7WUqFSAGtcMnRRv3+e+eEH+nz1yocY+H240srKgAunFO9QRgMVy6cE9lKFwi37/lhRZgw2IUnhJV7pFehZBkywn1hLUJ9XAiR0L13Ugio0D39gtWJDHNU6G3k17xxydCfj3PCCmprw2uv8kJghfBbVsie8znheLVwvKf38vAYmQROCEXC4Jz9LBCq/xQL2ZUwiq4XzTU64fw1R/XPQmFmPCfkFstJIRNNwXihsGDSoRCAE6q3CLukK8+/QEipTgnds1EsnN6hhL3V+UJS2yXM48KoQHdq4ubvhcLaClucVb1V2G4DnxN6LZcIPcyUerixwilqm/FvlLA9JPTjGQgHRNhzr9fQG2YSFTxPCsf7k2teis0KW1Z4WypAC3uhkL39pzR8nkVIhET4ygn1KuRuL07YUa3L0rmFea3QrSon7JgsZ4VQJJwf78LlUD4mnHdoxbiuEz75POQtKhfu9dWCzla4R15IhlxoB7LCUWF4XxhHoXA/M2q8Pdy16gphYXCXR0a4NiHCynt6sVCJhV6TSpLdzgm55a2ohhZ7aKjpYkLpEp+2FSM0aTk2YStKzwvNo1wYMIxXlPEeC9cRlQjq1+uAEEqEzdYWCBX2hakRrbDJCtHDhBHueS8S5qPO9jD+6omFQU9MSJ1agZA92mrRhBX/21smOkps4ttUC4XDXSIrE5rPrFCxQoMJZZsQ8sKfzwhtWOHHKvwpGnosKiqafRFKZ5oVfv4jFn59LF36AyWd488iIbBCcMLhYYXumN6Er+XHvVg4P2Jn11DCqGElFAobWrj3zC7oMMnw6y4kTuTPb57wI5pAVgip0EV/RJgyO//HR9H0lwHuxUJAhWoTtmz3D0lOXIhEr7PzbfZ1uC0OXDg8TJGwcfXJCbmtViTsoidMqf8UCLnQpLBJhXzwQvUu4RqzcOllHtcJhaHTlwuEUQ0MM1A0SC/09cnLz6B9qy4lnIs2PAqKURwZ4fCLJ1yfJEJtpMLnlUJbu1QYhYmErURohLDxK3PGbML9qKf38u9I2AoS031e7k9UqAuFiZgTirZloTAsUHSVp2fP8NiFrwuEIBVuujYRxrtzFYJUOE9dSdTCCIVT9l3Yo8L88WcmYSUY2eQnuws3yPR/NaZCby+/lvGBLVKpUGWELqzwzglhE95oxXuFvdouuiAi4dQzEbqvb/LrcL1BUyEb62bq8e6psOaFWFSRMCikXIi2mehOWYVrtImQiOYaIYiE/iuhUCmRcAvlC7Uce0hoh2SEyDOVpPO2QdGZyghBJCSuTFI4T0ehkyoS5obMC5GRPOHnO4RwVmhjsVlhf0Q4lt+72I0+EOeSjXcJiWPngNAGImyvEHZy4au5QhjvW0Me0JNwuKVCL/Zco0gIVwtnpFBo40qhiYWaFIJc2JcCkdjWoascK3S3dhriizh70W+2eyJ8eu+X36WdtKMpFfah0J+oKhn4zcIljgntvo06tm8RAip8CoWGEHYpyFwhdANIhfA2IdIUXCzHhBALW1yI32HeOUoKX2IhMItupnQPs/dJD6tFuJ0/ltsUCdUxofKEkBWaSKikwt69lBU2pND/qKvQeAvDE9ZO6Ch3X+jO+rpQCJQwjlQIvjB9WyCEY0I2EuHghLVQyO/PYHUfCTy9FQaLOhCqbIoyoeALzDmiszESBjslFfanhC+yRTPC5HQjhNIqGJmwLxb2mBAuEBqvsC/0z13Yosf8cF9bhUIoETaxEEghpELlhHCpEP7vhG58WbdYmK5rNX4xVxHB2wLQun33kghtpN/gRwv1pcKmRLgvrCoS9oVCbB12B4ReW3SSV1HPVIgd/aPChWvfI8JKHxM2lwqReHpCLRPCBcK+OSUsiuxQfbwONfLamF7YiVAdFIJUqITC8R42WdVwU2/hzcLhl0W4L5afID0BGCGkwvgykSyZfURkorFQyYSRdLgBKayoBi+eYmGFCpH4/sI92y2vQ4T+6KeEkBE2lUtTQ3y4Qng9vgghFeTlilx0YTUI4Y0SNpgQvpfQe44K9XXC1yEhRELghFgu8lSsk/l4wntOiJf/Nn/wuInb5dNAx4ThrlNiYa1bZu2cEQ7RVw2EYyUWglC4508/Vu3+bHtaGKbDhEi/OUn+3F5G3evGCuPk7xb2mDCNI0J0HRYnOSnMRXxfUaeNQAhO+MyX4oBw/Yzjl6o4Z588YYV+dy0UmnNC+L7C/wF2oMD2tQRU+wAAAABJRU5ErkJggg==

Mask used (BASE64 PNG encoded)

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--- Psycho-Visual Grain Equalization De-Noise
Parameter [Grain Size] set to [4.0 pixels]
--- Wavelet De-Noise
Parameter [Filter Type] set to [Distance Weighted Outlier Rejection]
Parameter [Grain Size] set to [4.0 pixels]
--- Wavelet De-Noise
Parameter [Scale 1] set to [95 %]
Parameter [Scale 2] set to [95 %]
Parameter [Scale 3] set to [95 %]
Parameter [Scale 4] set to [95 %]
Parameter [Scale 5] set to [95 %]
Parameter [Mask Fuzz] set to [1.0 pixels]
Parameter [Scale Correlation] set to [6]
Parameter set to [12 %]
Parameter [Brightness Detail Loss] set to [12 %]
Parameter [Grain Dispersion] set to [5.0 pixels]
Parameter [Non-linear Response <] set to [Off]
Parameter [Smoothness] set to [65 %]
File saved [C:\Users\User\Desktop\new cresent nebula 3 june\new startools 6 version 3.png].

June 5, 2020

also noticed this edition of Startools, i opened the one above, any idea what this one is ?

June 6, 2020

16 hours ago, red dwalf said:

also noticed this edition of Startools, i opened the one above, any idea what this one is ?

i think avx2 is a type of processor

Advanced Vector Extensions (AVX, also known as Sandy Bridge New Extensions) are extensions to the x86 instruction set architecture for microprocessors from Intel and AMD proposed by Intel in March 2008 and first supported by Intel with the Sandy Bridge[1] processor shipping in Q1 2011 and later on by AMD with the Bulldozer[2] processor shipping in Q3 2011. AVX provides new features, new instructions and a new coding scheme.

AVX2 expands most integer commands to 256 bits and introduces fused multiply-accumulate (FMA) operations. AVX-512 expands AVX to 512-bit support using a new EVEX prefix encoding proposed by Intel in July 2013 and first supported by Intel with the Knights Landing processor, which shipped in 2016.[3][4]

Edited June 6, 2020 by Bottletopburly

June 6, 2020

Using AVX instruction set enabled software can speed up applications to achieve better performance. I believe Intel ICore3/5/9 processors are compatible but Pentiums and Celerons aren't. It may be worth a try to see if you get better performance. At worst , if it complaints it isn't compatable you can go back to the non-AVX version. This is obviously one of the 'improvements' between version 1.5 and 1.6.

June 6, 2020

interesting, I did try and start the avx files but would not load, just disappeared.

Edited June 6, 2020 by red dwalf

June 6, 2020

I'll give it a try on my PC and let you know ...

June 6, 2020

On second dev use auto dev and use a roi play around with different areas ,manual dev works differently rob auto Dev will/should give better results , from startools website .

Usage

First off, please note that this module emulates many aspects of photographic film, including its shortcomings. These shortcomings include photographic film's tendency to "bloat" stellar profiles. If your goal is to achieve a non-linear stretch that shows as much detail as possible, the far more advanced AutoDev will always do an objectively better job for that purpose.

June 6, 2020

15 minutes ago, Clive said:

I'll give it a try on my PC and let you know ...

It runs on my laptop (Win10/I5) but I needed to ignore the warning that popped up to start it.

June 6, 2020

29 minutes ago, Bottletopburly said:

On second dev use auto dev and use a roi play around with different areas ,manual dev works differently rob auto Dev will/should give better results , from startools website .

Usage

First off, please note that this module emulates many aspects of photographic film, including its shortcomings. These shortcomings include photographic film's tendency to "bloat" stellar profiles. If your goal is to achieve a non-linear stretch that shows as much detail as possible, the far more advanced AutoDev will always do an objectively better job for that purpose.

nice one cheers, what do you think of the rest of the work flow?

June 6, 2020

42 minutes ago, red dwalf said:

nice one cheers, what do you think of the rest of the work flow?

looks good rob ,Dark anomaly in wipe i usually use 5 pixels and that will be the default in contrast ,5 pixels is usually what ivo has used when i have asked him anything , in the denoise module the one you used you can experiment how smooth you want sometimes too smooth looks unatural so you can play to add a slight graininess obviously only as good as your monitor shows you , for me the first three steps are the most important Auto dev ,Wipe Auto dev .

not that i,am an expert on startools

Dave

June 6, 2020

all helps Dave, cheers

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version 6 of Startools used and reprocessing of Crescent nebula in HA

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