论文标题
LCA-NET：图像去悬式的轻卷卷积自动编码器
LCA-Net: Light Convolutional Autoencoder for Image Dehazing
论文作者
论文摘要
Dimage Dehazing是一项至关重要的图像预处理任务，旨在消除阴霾产生的不一致的噪声，以改善图像的视觉吸引力。现有模型使用复杂的网络和自定义损失功能，这些功能在计算上效率低下，需要重型硬件才能运行。时间是图像预处理的本质，因为可以立即获得实时输出。为了克服这些问题，我们提出的通用模型使用了非常轻的卷积编码器网络，该网络不取决于任何大气模型。网络复杂性图像质量在这个神经网络中可以很好地处理，并且该网络的性能不受低规格系统的限制。该网络在几个标准数据集上以更快的速度实现了最佳的飞行性能，就图像质量而言，可与最先进的方法相媲美。
Image dehazing is a crucial image pre-processing task aimed at removing the incoherent noise generated by haze to improve the visual appeal of the image. The existing models use sophisticated networks and custom loss functions which are computationally inefficient and requires heavy hardware to run. Time is of the essence in image pre-processing since real time outputs can be obtained instantly. To overcome these problems, our proposed generic model uses a very light convolutional encoder-decoder network which does not depend on any atmospheric models. The network complexity-image quality trade off is handled well in this neural network and the performance of this network is not limited by low-spec systems. This network achieves optimum dehazing performance at a much faster rate, on several standard datasets, comparable to the state-of-the-art methods in terms of image quality.
