Fully End-to-End learning based Conditional Boundary Equilibrium GAN with Receptive Field Sizes Enlarged for Single Ultra-High Resolution Image Dehazing

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dc.contributor.authorKi, Sehwanko
dc.contributor.authorSIM, HYEONJUNko
dc.contributor.authorChoi, Jae Seokko
dc.contributor.authorKim, Soo Yeko
dc.contributor.authorSeo, Soominko
dc.contributor.authorKim, Saehunko
dc.contributor.authorKim, Munchurlko
dc.date.accessioned2018-12-20T02:13:24Z-
dc.date.available2018-12-20T02:13:24Z-
dc.date.created2018-12-01-
dc.date.created2018-12-01-
dc.date.created2018-12-01-
dc.date.issued2018-06-18-
dc.identifier.citationIEEE Computer Vision and Pattern Recognition Workshops (CVPRW), pp.930 - 937-
dc.identifier.urihttp://hdl.handle.net/10203/247467-
dc.description.abstractA receptive field is defined as the region in an input image space that an output image pixel is looking at. Thus, the receptive field size influences the learning of deep convolution neural networks. Especially, in single image dehazing problems, larger receptive fields often show more effective dehazying by considering the brightness and color of the entire input hazy image without additional information (e.g. scene transmission map, depth map, and atmospheric light). The conventional generative adversarial network (GAN) with small-sized receptive fields cannot be effective for hazy images of ultra-high resolution. Thus, we proposed a fully end-to-end learning based conditional boundary equilibrium generative adversarial network (BEGAN) with the receptive field sizes enlarged for single image dehazing. In our conditional BEGAN, its discriminator is trained ultra-high resolution conditioned on downscale input hazy images, so that the haze can effectively be removed with the original structures of images stably preserved. From this, we can obtain the high PSNR performance (Track 1 - Indoor: top 4th-ranked) and fast computation speeds. Also, we combine an L1 loss, a perceptual loss and a GAN loss as the generator’s loss of the proposed conditional BEGAN, which allows to obtain stable dehazing results for various hazy images.-
dc.languageEnglish-
dc.publisherIEEE Computer Society and the Computer Vision Foundation (CVF)-
dc.titleFully End-to-End learning based Conditional Boundary Equilibrium GAN with Receptive Field Sizes Enlarged for Single Ultra-High Resolution Image Dehazing-
dc.typeConference-
dc.identifier.wosid000457636800119-
dc.identifier.scopusid2-s2.0-85060852486-
dc.type.rimsCONF-
dc.citation.beginningpage930-
dc.citation.endingpage937-
dc.citation.publicationnameIEEE Computer Vision and Pattern Recognition Workshops (CVPRW)-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationSalt Lake City, UT, USA-
dc.identifier.doi10.1109/CVPRW.2018.00126-
dc.contributor.localauthorKim, Munchurl-
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