基于生成对抗网络的图像清晰度提升方法

视频监控、军事目标识别以及消费型摄影等众多领域对图像清晰度有很高的要求.近年来,深度神经网络在视觉和定量评估的应用研究中取得较大进展,但是其结果一般缺乏图像纹理的细节,边缘过度平滑,给人一种模糊的视觉体验.本文提出了一种基于生成对抗网络的图像清晰度提升方法.为了更好的传递图像的细节信息,采用改进的残差块和跳跃连接作为生成网络的主体架构,生成器损失函数除了对抗损失,还包括内容损失、感知损失和纹理损失.在DIV2K数据集上的实验表明,该方法在提升图像清晰度方面有较好的视觉体验和定量评估.

Dense Text Detection Method in Natural Scene

Text detection in natural scenes is one of the difficulties in the field of image processing. An efficient and accurate scene text detector (EAST) algorithm is an excellent text detection algorithm in recent years, but the AdvancedEAST algorithm after the addition of post processing still has the problem of missed detection caused by the loss of the head and tail boundaries of the activated pixels. Thus, the detection effect of dense texts is not ideal. For this reason, an improved algorithm of dilated-corner attention EAST (DCA_EAST) is proposed, and a dilated convolution module and a corner attention module are added to the network structure to improve the missed detection. For the loss function, weight factors of category and sample difficulty are introduced to effectively improve the detection effect of dense texts. The experimental results show that the proposed algorithm has an accuracy of 93.02%, a recall rate of 76.69%, and an F-measured value of 84.07% on the ReCTS dataset of ICDAR2019, thus being superior to the AdvancedEAST algorithm.