Matches in SemOpenAlex for { <https://semopenalex.org/work/W3091228418> ?p ?o ?g. }
- W3091228418 abstract "Deep convolutional neural networks perform better on images containing spatially invariant degradations, also known as synthetic degradations; however, their performance is limited on real-degraded photographs and requires multiple-stage network modeling. To advance the practicability of restoration algorithms, this paper proposes a novel single-stage blind real image restoration network (R$^2$Net) by employing a modular architecture. We use a residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality for four restoration tasks i.e. Denoising, Super-resolution, Raindrop Removal, and JPEG Compression on 11 real degraded datasets against more than 30 state-of-the-art algorithms demonstrate the superiority of our R$^2$Net. We also present the comparison on three synthetically generated degraded datasets for denoising to showcase the capability of our method on synthetics denoising. The codes, trained models, and results are available on this https URL." @default.
- W3091228418 created "2020-10-08" @default.
- W3091228418 creator A5060335798 @default.
- W3091228418 creator A5072837153 @default.
- W3091228418 creator A5084633048 @default.
- W3091228418 date "2020-04-26" @default.
- W3091228418 modified "2023-10-08" @default.
- W3091228418 title "Attention Based Real Image Restoration" @default.
- W3091228418 cites W1485262465 @default.
- W3091228418 cites W1514535095 @default.
- W3091228418 cites W1588663000 @default.
- W3091228418 cites W1791560514 @default.
- W3091228418 cites W180758837 @default.
- W3091228418 cites W1885185971 @default.
- W3091228418 cites W1906770428 @default.
- W3091228418 cites W1930824406 @default.
- W3091228418 cites W1970863638 @default.
- W3091228418 cites W1974042113 @default.
- W3091228418 cites W1978749115 @default.
- W3091228418 cites W1980414590 @default.
- W3091228418 cites W1985050490 @default.
- W3091228418 cites W1993205988 @default.
- W3091228418 cites W1997147589 @default.
- W3091228418 cites W2017132642 @default.
- W3091228418 cites W2025328853 @default.
- W3091228418 cites W2037133587 @default.
- W3091228418 cites W2037642501 @default.
- W3091228418 cites W2038133417 @default.
- W3091228418 cites W2045079989 @default.
- W3091228418 cites W2047710600 @default.
- W3091228418 cites W2047920195 @default.
- W3091228418 cites W2048695508 @default.
- W3091228418 cites W2049340374 @default.
- W3091228418 cites W2056370875 @default.
- W3091228418 cites W2058005980 @default.
- W3091228418 cites W2097073572 @default.
- W3091228418 cites W2097713019 @default.
- W3091228418 cites W2103544474 @default.
- W3091228418 cites W2107878631 @default.
- W3091228418 cites W2113968972 @default.
- W3091228418 cites W2117787067 @default.
- W3091228418 cites W2121927366 @default.
- W3091228418 cites W2122086266 @default.
- W3091228418 cites W2130184048 @default.
- W3091228418 cites W2130975789 @default.
- W3091228418 cites W2135065661 @default.
- W3091228418 cites W2142683286 @default.
- W3091228418 cites W2147527908 @default.
- W3091228418 cites W2154815154 @default.
- W3091228418 cites W2160547390 @default.
- W3091228418 cites W2172275395 @default.
- W3091228418 cites W2177195834 @default.
- W3091228418 cites W2194775991 @default.
- W3091228418 cites W2207282238 @default.
- W3091228418 cites W2214802144 @default.
- W3091228418 cites W2469031810 @default.
- W3091228418 cites W2503339013 @default.
- W3091228418 cites W2507211415 @default.
- W3091228418 cites W2508457857 @default.
- W3091228418 cites W2613155248 @default.
- W3091228418 cites W2740178346 @default.
- W3091228418 cites W2740494144 @default.
- W3091228418 cites W2741137940 @default.
- W3091228418 cites W2747898905 @default.
- W3091228418 cites W2771208044 @default.
- W3091228418 cites W2780544323 @default.
- W3091228418 cites W2795722336 @default.
- W3091228418 cites W2799192307 @default.
- W3091228418 cites W2820727372 @default.
- W3091228418 cites W2866634454 @default.
- W3091228418 cites W2884569173 @default.
- W3091228418 cites W2892465576 @default.
- W3091228418 cites W2899771611 @default.
- W3091228418 cites W2903251150 @default.
- W3091228418 cites W2952323569 @default.
- W3091228418 cites W2962767526 @default.
- W3091228418 cites W2963073614 @default.
- W3091228418 cites W2963315679 @default.
- W3091228418 cites W2963372104 @default.
- W3091228418 cites W2963420686 @default.
- W3091228418 cites W2963446712 @default.
- W3091228418 cites W2963614749 @default.
- W3091228418 cites W2963645458 @default.
- W3091228418 cites W2963725279 @default.
- W3091228418 cites W2963800716 @default.
- W3091228418 cites W2964046397 @default.
- W3091228418 cites W2964101377 @default.
- W3091228418 cites W2964121744 @default.
- W3091228418 cites W2964277374 @default.
- W3091228418 cites W2983315964 @default.
- W3091228418 cites W2987869089 @default.
- W3091228418 cites W3182211633 @default.
- W3091228418 hasPublicationYear "2020" @default.
- W3091228418 type Work @default.
- W3091228418 sameAs 3091228418 @default.
- W3091228418 citedByCount "0" @default.
- W3091228418 crossrefType "posted-content" @default.
- W3091228418 hasAuthorship W3091228418A5060335798 @default.
- W3091228418 hasAuthorship W3091228418A5072837153 @default.
- W3091228418 hasAuthorship W3091228418A5084633048 @default.