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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A 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.
Publisher
IEEE Computer Society and the Computer Vision Foundation (CVF)
Issue Date
2018-06-18
Language
English
Citation

IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), pp.930 - 937

DOI
10.1109/CVPRW.2018.00126
URI
http://hdl.handle.net/10203/247467
Appears in Collection
EE-Conference Papers(학술회의논문)
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