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+tasks%252C+their+performance+is+sensitive+to+the+choice+of+training+datasets.+Typically%252C+diffusion+models+trained+in+specific+datasets+fail+to+recover+images+that+have+out-of-distribution+degradations.+To+address+this+problem%252C+this+work+leverages+a+capable+vision-language+model+and+a+synthetic+degradation+pipeline+to+learn+image+restoration+in+the+wild+(wild+IR).+More+specifically%252C+all+low-quality+images+are+simulated+with+a+synthetic+degradation+pipeline+that+contains+multiple+common+degradations+such+as+blur%252C+resize%252C+noise%252C+and+JPEG+compression.+Then+we+introduce+robust+training+for+a+degradation-aware+CLIP+model+to+extract+enriched+image+content+features+to+assist+high-quality+image+restoration.+Our+base+diffusion+model+is+the+image+restoration+SDE+(IR-SDE).+Built+upon+it%252C+we+further+present+a+posterior+sampling+strategy+for+fast+noise-free+image+generation.+We+evaluate+our+model+on+both+synthetic+and+real-world+degradation+datasets.+Moreover%252C+experiments+on+the+unified+image+restoration+task+illustrate+that+the+proposed+posterior+sampling+improves+image+generation+quality+for+various+degradations.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.187bd7944093c44b04b3842872aaac51%3frik%3dtDUoaa633JDwZQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Image+Restoration+in+Machine+Learning&FORM=IRPRST&ck=8B07A234016C56E6D70B160F6986DB91&selectedIndex=34&itb=0)
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