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+is+a+classic+yet+challenging+problem+in+the+field+of+image+processing.+Recent+advances+in+deep+image+prior+(DIP)+have+motivated+a+series+of+DIP-based+approaches%252C+demonstrating+remarkable+success+in+BID.+However%252C+due+to+the+high+non-convexity+of+the+inherent+optimization+process%252C+these+methods+are+notorious+for+their+sensitivity+to+the+initialized+kernel.+To+alleviate+this+issue+and+further+improve+their+performance%252C+we+propose+a+new+framework+for+BID+that+better+considers+the+prior+modeling+and+the+initialization+for+blur+kernels%252C+leveraging+a+deep+generative+model.+The+proposed+approach+pre-trains+a+generative+adversarial+network-based+kernel+generator+that+aptly+characterizes+the+kernel+priors+and+a+kernel+initializer+that+facilitates+a+well-informed+initialization+for+the+blur+kernel+through+latent+space+encoding.+With+the+pre-trained+kernel+generator+and+initializer%252C+one+can+obtain+a+high-quality+initialization+of+the+blur+kernel%252C+and+enable+optimization+within+a+compact+latent+kernel+manifold.+Such+a+framework+results+in+an+evident+performance+improvement+over+existing+DIP-based+BID+methods.+Extensive+experiments+on+different+datasets+demonstrate+the+effectiveness+of+the+proposed+method.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.a7866f8af98eb20a821b05af02b3464a%3frik%3dcJEIPrSftjk6Jw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Blind+Image+Deconvolution&FORM=IRPRST&ck=FC851AEC5D14C2C22E07F9478E79DB54&selectedIndex=21&itb=0)
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