High-Resolution Image Dehazing with Respect to Training Losses and Receptive Field Sizes

Citations Over TimeTop 15% of 2018 papers

Abstract

Haze removal is one of the essential image enhancement processes that makes degraded images visually pleasing. Since haze in images often appears differently depending on the surroundings, haze removal requires the use of spatial information to effectively remove the haze according to the types of image region characteristics. However, in the conventional training, the small-sized training image patches could not provide spatial information to the training networks when they are relatively very small compared to the original training image resolutions. In this paper, we propose a simple but effective network for high-resolution image dehazing using a conditional generative adversarial network (CGAN), which is called DeHazing GAN (DHGAN), where the hazy patches of scale-reduced training input images are applied to the generator network of DHGAN. By doing so, DHGAN can capture more global features of the haziness in the training image patches, thus leading to improved dehazing performance. Also, DHGAN is trained based on the largest binary cross entropy loss among the multiple outputs so that the generator network of DHGAN can favorably be trained in accordance with perceptual quality. From extensive training and test, our proposed DHGAN was ranked in the second place for the NTIRE2018 Image Dehazing Challenge Track2: Outdoor.

Related Papers

• → Secondary Formation of Sulfate and Nitrate during a Haze Episode in Megacity Beijing, China(2015)88 cited
• → Identification of chemical compositions and sources of atmospheric aerosols in Xi'an, inland China during two types of haze events(2016)59 cited
• → Image restoration using prior information physics model(2011)4 cited
• The Effects of Dust Haze Weather on Human Health(2008)
• A Study on Long-term Change of Haze Days Based on Wavelet Transform and M-K Test(2012)