Restoration and reconstruction of AVHRR images
Describes the design of small convolution kernels for the restoration and reconstruction of Advanced Very High Resolution Radiometer (AVHRR) images. The kernels are small enough to be implemented efficiently by convolution, yet effectively correct degradations and increase apparent resolution. The k...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 1995-07, Vol.33 (4), p.997-1007 |
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| container_end_page | 1007 |
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| container_issue | 4 |
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| container_title | IEEE transactions on geoscience and remote sensing |
| container_volume | 33 |
| creator | Reichenbach, S.E. Koehler, D.E. Strelow, D.W. |
| description | Describes the design of small convolution kernels for the restoration and reconstruction of Advanced Very High Resolution Radiometer (AVHRR) images. The kernels are small enough to be implemented efficiently by convolution, yet effectively correct degradations and increase apparent resolution. The kernel derivation is based on a comprehensive, end-to-end system model that accounts for scene statistics, image acquisition blur, sampling effects, sensor noise, and postfilter reconstruction. The design maximizes image fidelity subject to explicit constraints on the spatial support and resolution of the kernel. The kernels can be designed with finer resolution than the image to perform partial reconstruction for geometric correction and other remapping operations. Experiments demonstrate that small kernels yield fidelity comparable to optimal unconstrained filters with less computation.< > |
| doi_str_mv | 10.1109/36.406685 |
| format | Article |
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The kernels are small enough to be implemented efficiently by convolution, yet effectively correct degradations and increase apparent resolution. The kernel derivation is based on a comprehensive, end-to-end system model that accounts for scene statistics, image acquisition blur, sampling effects, sensor noise, and postfilter reconstruction. The design maximizes image fidelity subject to explicit constraints on the spatial support and resolution of the kernel. The kernels can be designed with finer resolution than the image to perform partial reconstruction for geometric correction and other remapping operations. Experiments demonstrate that small kernels yield fidelity comparable to optimal unconstrained filters with less computation.< ></description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/36.406685</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Areal geology. Maps ; Convolution ; Degradation ; Earth sciences ; Earth, ocean, space ; Exact sciences and technology ; External geophysics ; Geologic maps, cartography ; Geophysics. 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The kernels are small enough to be implemented efficiently by convolution, yet effectively correct degradations and increase apparent resolution. The kernel derivation is based on a comprehensive, end-to-end system model that accounts for scene statistics, image acquisition blur, sampling effects, sensor noise, and postfilter reconstruction. The design maximizes image fidelity subject to explicit constraints on the spatial support and resolution of the kernel. The kernels can be designed with finer resolution than the image to perform partial reconstruction for geometric correction and other remapping operations. Experiments demonstrate that small kernels yield fidelity comparable to optimal unconstrained filters with less computation.< ></description><subject>Areal geology. Maps</subject><subject>Convolution</subject><subject>Degradation</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Geologic maps, cartography</subject><subject>Geophysics. Techniques, methods, instrumentation and models</subject><subject>Image reconstruction</subject><subject>Image resolution</subject><subject>Image restoration</subject><subject>Kernel</subject><subject>Layout</subject><subject>Radiometry</subject><subject>Spatial resolution</subject><subject>Statistics</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1995</creationdate><recordtype>article</recordtype><recordid>eNqFkE1LAzEQhoMoWKsHr572IIKHrZl851iKWqEgFPUa0jgrK9vdmmwP_ntTt_Ta08DMMy8zDyHXQCcA1D5wNRFUKSNPyAikNCVVQpySEQWrSmYsOycXKX1TCkKCHpHJElPfRd_XXVv49rOIGLo29XEb_ltdVUw_5stlUa_9F6ZLclb5JuHVvo7J-9Pj22xeLl6fX2bTRRkE5X0ppFqB4UEzUxnKNYYAGrTU3GDFPCAg89oqvlIUGK8Q89hSZXBlWJCMj8ndkLuJ3c82n-jWdQrYNL7Fbpscs5QpTdVx0EhhlRTHQWk1z5IyeD-AIXYpRazcJubn468D6naOHVducJzZ232oT8E3VfRtqNNhgWf3oHbYzYDViHiY7jP-AI2ngEI</recordid><startdate>19950701</startdate><enddate>19950701</enddate><creator>Reichenbach, S.E.</creator><creator>Koehler, D.E.</creator><creator>Strelow, D.W.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>19950701</creationdate><title>Restoration and reconstruction of AVHRR images</title><author>Reichenbach, S.E. ; Koehler, D.E. ; Strelow, D.W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c403t-456b183c728f8037ecc17175738ef2a1e1e2a7963b60123fee1719068eb82c523</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Areal geology. 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Techniques, methods, instrumentation and models</topic><topic>Image reconstruction</topic><topic>Image resolution</topic><topic>Image restoration</topic><topic>Kernel</topic><topic>Layout</topic><topic>Radiometry</topic><topic>Spatial resolution</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Reichenbach, S.E.</creatorcontrib><creatorcontrib>Koehler, D.E.</creatorcontrib><creatorcontrib>Strelow, D.W.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Reichenbach, S.E.</au><au>Koehler, D.E.</au><au>Strelow, D.W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Restoration and reconstruction of AVHRR images</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>1995-07-01</date><risdate>1995</risdate><volume>33</volume><issue>4</issue><spage>997</spage><epage>1007</epage><pages>997-1007</pages><issn>0196-2892</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract>Describes the design of small convolution kernels for the restoration and reconstruction of Advanced Very High Resolution Radiometer (AVHRR) images. 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| ispartof | IEEE transactions on geoscience and remote sensing, 1995-07, Vol.33 (4), p.997-1007 |
| issn | 0196-2892 1558-0644 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Areal geology. Maps Convolution Degradation Earth sciences Earth, ocean, space Exact sciences and technology External geophysics Geologic maps, cartography Geophysics. Techniques, methods, instrumentation and models Image reconstruction Image resolution Image restoration Kernel Layout Radiometry Spatial resolution Statistics |
| title | Restoration and reconstruction of AVHRR images |
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