The issue of image haze removal has attracted wide attention in recent years. However, most existing haze removal methods cannot restore the scene with clear blue sky, since the color and texture information of the object in the original haze image is insufficient. To remedy this, we propose a cycle generative adversarial network to construct a novel end-to-end image dehaze model. We adopt outdoor image datasets to train our model, which includes a set of real-world unpaired image dataset and a set of paired image dataset to ensure that the generated images are close to the real scene. Based on the cycle structure, our model adds four different kinds of loss function to constrain the effect including adversarial loss, cycle consistency loss, photorealism loss and paired L1 loss. These four constraints can improve the overall quality of such degraded images for better visual appeal and ensure reconstruction of images to keep from distortion. The proposed model could remove the haze of images and also restore the sky of images to be clean and blue (like captured in a sunny weather).
翻译:然而,大多数现有的烟雾清除方法无法以清晰的蓝天恢复场景,因为原始烟雾图像中对象的颜色和纹理信息不够充分。为了纠正这一点,我们提议建立一个循环基因对抗网络,以构建一个新的端到端图像脱色模型。我们采用户外图像数据集来培训我们的模型,其中包括一套真实世界未受光化图像数据集和一套配对图像数据集,以确保生成的图像接近真实场景。根据循环结构,我们的模型增加了四种不同的损失功能,以限制其影响,包括对抗性损失、周期一致性损失、光现实主义损失和配对L1损失。这四种限制因素可以提高这种退化图像的总体质量,以便更好的视觉吸引力,并确保图像的重建不受扭曲。拟议的模型可以消除图像的烟雾,并使图像的天空恢复清洁和蓝色(如阳光天气所捕捉到的)。