Underwater image dehazing using joint trilateral filter

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NAS-Unet: Neural Architecture Search for Medical Image Segmentation (2019, methodology): “For the sake of fairness, we re-implement them by Pytorch [55] and use the same data augmentation(we also try to improve the quality of some noisy images [56], [57]).”
Color Balance and Fusion for Underwater Image Enhancement (2018, background): “To complete our discussion about the severe and colordependent attenuation of light under water, it is worth noting the works in [31] and [46]–[48], which reveal and exploit the fact that, in turbid waters or in places with high concentration of plankton, the blue channel may be significantly attenu”
Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior (2016, unknown): “Serikawa and Lu [20] solved the problems of scattering and color deviation for underwater images by compensating the attenuation discrepancy along the propagation path.”
Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map (2019, background): “(11) Due to the aggressive attenuation of the red channel in underwater images, the DCPgb that considers only G and B channels was defined as underwater DCP (UDCP) [22], [23].”
Automatic Red-Channel underwater image restoration (2015, methodology): “With any of the three possibilities, the hypothesis of the theorem is verified.”