Machine learning generation of low-noise and high structural conspicuity images
Systems/techniques that facilitate machine learning generation of low-noise and high structural conspicuity images are provided. In various embodiments, a system can access an image and can apply at least one of image denoising or image resolution enhancement to the image, thereby yielding a first i...
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| creator | Imai, Yasuhiro Das, Bipul Langoju, Rajesh Veera Venkata Lakshmi Agrawal, Utkarsh Shigemasa, Risa Hsieh, Jiang |
| description | Systems/techniques that facilitate machine learning generation of low-noise and high structural conspicuity images are provided. In various embodiments, a system can access an image and can apply at least one of image denoising or image resolution enhancement to the image, thereby yielding a first intermediary image. In various instances, the system can generate, via execution of a plurality of machine learning models, a plurality of second intermediary images based on the first intermediary image, wherein a given machine learning model in the plurality of machine learning models receives as input the first intermediary image, wherein the given machine learning model produces as output a given second intermediary image in the plurality of second intermediary images, and wherein the given second intermediary image represents a kernel-transformed version of the first intermediary image. In various cases, the system can generate a blended image based on the plurality of second intermediary images. |
| format | Patent |
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In various embodiments, a system can access an image and can apply at least one of image denoising or image resolution enhancement to the image, thereby yielding a first intermediary image. In various instances, the system can generate, via execution of a plurality of machine learning models, a plurality of second intermediary images based on the first intermediary image, wherein a given machine learning model in the plurality of machine learning models receives as input the first intermediary image, wherein the given machine learning model produces as output a given second intermediary image in the plurality of second intermediary images, and wherein the given second intermediary image represents a kernel-transformed version of the first intermediary image. In various cases, the system can generate a blended image based on the plurality of second intermediary images.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20241112&DB=EPODOC&CC=US&NR=12141900B2$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20241112&DB=EPODOC&CC=US&NR=12141900B2$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Imai, Yasuhiro</creatorcontrib><creatorcontrib>Das, Bipul</creatorcontrib><creatorcontrib>Langoju, Rajesh Veera Venkata Lakshmi</creatorcontrib><creatorcontrib>Agrawal, Utkarsh</creatorcontrib><creatorcontrib>Shigemasa, Risa</creatorcontrib><creatorcontrib>Hsieh, Jiang</creatorcontrib><title>Machine learning generation of low-noise and high structural conspicuity images</title><description>Systems/techniques that facilitate machine learning generation of low-noise and high structural conspicuity images are provided. In various embodiments, a system can access an image and can apply at least one of image denoising or image resolution enhancement to the image, thereby yielding a first intermediary image. In various instances, the system can generate, via execution of a plurality of machine learning models, a plurality of second intermediary images based on the first intermediary image, wherein a given machine learning model in the plurality of machine learning models receives as input the first intermediary image, wherein the given machine learning model produces as output a given second intermediary image in the plurality of second intermediary images, and wherein the given second intermediary image represents a kernel-transformed version of the first intermediary image. In various cases, the system can generate a blended image based on the plurality of second intermediary images.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNzD0KAjEQQOFtLES9w3iAhc1qY7ui2IiFWi9DnE0G4kzID-LttfAAVq_5ePPmckbrWQgCYRIWB46EEhZWAZ0g6KsV5UyA8gDPzkMuqdpSEwawKjmyrVzewE90lJfNbMKQafXrolkfD7f9qaWoI-WI9rsv4_1qerM1u64b-s0_5gOBdzdt</recordid><startdate>20241112</startdate><enddate>20241112</enddate><creator>Imai, Yasuhiro</creator><creator>Das, Bipul</creator><creator>Langoju, Rajesh Veera Venkata Lakshmi</creator><creator>Agrawal, Utkarsh</creator><creator>Shigemasa, Risa</creator><creator>Hsieh, Jiang</creator><scope>EVB</scope></search><sort><creationdate>20241112</creationdate><title>Machine learning generation of low-noise and high structural conspicuity images</title><author>Imai, Yasuhiro ; Das, Bipul ; Langoju, Rajesh Veera Venkata Lakshmi ; Agrawal, Utkarsh ; Shigemasa, Risa ; Hsieh, Jiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US12141900B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Imai, Yasuhiro</creatorcontrib><creatorcontrib>Das, Bipul</creatorcontrib><creatorcontrib>Langoju, Rajesh Veera Venkata Lakshmi</creatorcontrib><creatorcontrib>Agrawal, Utkarsh</creatorcontrib><creatorcontrib>Shigemasa, Risa</creatorcontrib><creatorcontrib>Hsieh, Jiang</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Imai, Yasuhiro</au><au>Das, Bipul</au><au>Langoju, Rajesh Veera Venkata Lakshmi</au><au>Agrawal, Utkarsh</au><au>Shigemasa, Risa</au><au>Hsieh, Jiang</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Machine learning generation of low-noise and high structural conspicuity images</title><date>2024-11-12</date><risdate>2024</risdate><abstract>Systems/techniques that facilitate machine learning generation of low-noise and high structural conspicuity images are provided. In various embodiments, a system can access an image and can apply at least one of image denoising or image resolution enhancement to the image, thereby yielding a first intermediary image. In various instances, the system can generate, via execution of a plurality of machine learning models, a plurality of second intermediary images based on the first intermediary image, wherein a given machine learning model in the plurality of machine learning models receives as input the first intermediary image, wherein the given machine learning model produces as output a given second intermediary image in the plurality of second intermediary images, and wherein the given second intermediary image represents a kernel-transformed version of the first intermediary image. In various cases, the system can generate a blended image based on the plurality of second intermediary images.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
| title | Machine learning generation of low-noise and high structural conspicuity images |
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