Image correction method based on deep learning
The invention discloses an image correction method based on deep learning, and the method comprises the steps: (1), photographing images of the same field of view as much as possible through employing image collection equipment with color difference and good image collection equipment, and taking th...
Gespeichert in:
Hauptverfasser: | , , , , |
---|---|
Format: | Patent |
Sprache: | chi ; eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | LIU DONG QIAN CHANGDE WANG YUE SUN HUANYU LEI JIARUI |
description | The invention discloses an image correction method based on deep learning, and the method comprises the steps: (1), photographing images of the same field of view as much as possible through employing image collection equipment with color difference and good image collection equipment, and taking the images as a color difference image and a reference image; (2) solving offset of two times of shooting by using a template matching algorithm, cutting the two images according to the offset, and further dividing into a training set and a test set; (3) constructing an image correction model which comprises a weight prediction network and n learnable 3D lookup tables; (4) inputting an image with chromatic aberration into the network, comparing the corrected image with a reference image, and calculating a loss function; training by taking loss function minimization as a target, and updating network parameters; and (5) after model training is completed, image correction application is carried out. According to the met |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN114463196A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN114463196A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN114463196A3</originalsourceid><addsrcrecordid>eNrjZNDzzE1MT1VIzi8qSk0uyczPU8hNLcnIT1FISixOTVEA8lNSUwsUclITi_Iy89J5GFjTEnOKU3mhNDeDoptriLOHbmpBfnxqcUFicmpeakm8s5-hoYmJmbGhpZmjMTFqANjaKkE</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Image correction method based on deep learning</title><source>esp@cenet</source><creator>LIU DONG ; QIAN CHANGDE ; WANG YUE ; SUN HUANYU ; LEI JIARUI</creator><creatorcontrib>LIU DONG ; QIAN CHANGDE ; WANG YUE ; SUN HUANYU ; LEI JIARUI</creatorcontrib><description>The invention discloses an image correction method based on deep learning, and the method comprises the steps: (1), photographing images of the same field of view as much as possible through employing image collection equipment with color difference and good image collection equipment, and taking the images as a color difference image and a reference image; (2) solving offset of two times of shooting by using a template matching algorithm, cutting the two images according to the offset, and further dividing into a training set and a test set; (3) constructing an image correction model which comprises a weight prediction network and n learnable 3D lookup tables; (4) inputting an image with chromatic aberration into the network, comparing the corrected image with a reference image, and calculating a loss function; training by taking loss function minimization as a target, and updating network parameters; and (5) after model training is completed, image correction application is carried out. According to the met</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PHYSICS</subject><creationdate>2022</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=20220510&DB=EPODOC&CC=CN&NR=114463196A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,778,883,25547,76298</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220510&DB=EPODOC&CC=CN&NR=114463196A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LIU DONG</creatorcontrib><creatorcontrib>QIAN CHANGDE</creatorcontrib><creatorcontrib>WANG YUE</creatorcontrib><creatorcontrib>SUN HUANYU</creatorcontrib><creatorcontrib>LEI JIARUI</creatorcontrib><title>Image correction method based on deep learning</title><description>The invention discloses an image correction method based on deep learning, and the method comprises the steps: (1), photographing images of the same field of view as much as possible through employing image collection equipment with color difference and good image collection equipment, and taking the images as a color difference image and a reference image; (2) solving offset of two times of shooting by using a template matching algorithm, cutting the two images according to the offset, and further dividing into a training set and a test set; (3) constructing an image correction model which comprises a weight prediction network and n learnable 3D lookup tables; (4) inputting an image with chromatic aberration into the network, comparing the corrected image with a reference image, and calculating a loss function; training by taking loss function minimization as a target, and updating network parameters; and (5) after model training is completed, image correction application is carried out. According to the met</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>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZNDzzE1MT1VIzi8qSk0uyczPU8hNLcnIT1FISixOTVEA8lNSUwsUclITi_Iy89J5GFjTEnOKU3mhNDeDoptriLOHbmpBfnxqcUFicmpeakm8s5-hoYmJmbGhpZmjMTFqANjaKkE</recordid><startdate>20220510</startdate><enddate>20220510</enddate><creator>LIU DONG</creator><creator>QIAN CHANGDE</creator><creator>WANG YUE</creator><creator>SUN HUANYU</creator><creator>LEI JIARUI</creator><scope>EVB</scope></search><sort><creationdate>20220510</creationdate><title>Image correction method based on deep learning</title><author>LIU DONG ; QIAN CHANGDE ; WANG YUE ; SUN HUANYU ; LEI JIARUI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN114463196A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</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>LIU DONG</creatorcontrib><creatorcontrib>QIAN CHANGDE</creatorcontrib><creatorcontrib>WANG YUE</creatorcontrib><creatorcontrib>SUN HUANYU</creatorcontrib><creatorcontrib>LEI JIARUI</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LIU DONG</au><au>QIAN CHANGDE</au><au>WANG YUE</au><au>SUN HUANYU</au><au>LEI JIARUI</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Image correction method based on deep learning</title><date>2022-05-10</date><risdate>2022</risdate><abstract>The invention discloses an image correction method based on deep learning, and the method comprises the steps: (1), photographing images of the same field of view as much as possible through employing image collection equipment with color difference and good image collection equipment, and taking the images as a color difference image and a reference image; (2) solving offset of two times of shooting by using a template matching algorithm, cutting the two images according to the offset, and further dividing into a training set and a test set; (3) constructing an image correction model which comprises a weight prediction network and n learnable 3D lookup tables; (4) inputting an image with chromatic aberration into the network, comparing the corrected image with a reference image, and calculating a loss function; training by taking loss function minimization as a target, and updating network parameters; and (5) after model training is completed, image correction application is carried out. According to the met</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN114463196A |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
title | Image correction method based on deep learning |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T08%3A46%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=LIU%20DONG&rft.date=2022-05-10&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN114463196A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |