【目的】 针对雾霾天气下林地无人机航拍图像存在对比度低、饱和度低和色调偏移等现象,基于Resnet网络,提出一种适应林地航拍场景的无人机图像去雾方法(DHnet)。【方法】 林地场景下无人机图像具有纹理特征、高低频信息丰富的特点,在主干网络各个层级附加信息传递模块,将特征图转化为权值图进行筛选过滤并发送到其他层级,接收端设置阈值避免冗余信息的不良影响,再经密集链接增强全局去雾效果,提高图像高低频区域的去雾质量,最后在林地无人机有雾图像测试集上进行去雾实验。【结果】 DHnet在林地图像测试集上的平均结构相似性为0.83,平均峰值信噪比为22.3 dB,分别较Resnet方法提高了4.8%和39.3%。【结论】 本研究提出的算法能有效降低图像色调偏移,去除残留雾气信息,有效提高无人机航拍林地雾气图像的色彩保真度和细节信息保持度。

【Objective】 Aiming to address the phenomena of low contrast, low saturation, and hue shift in unmanned aerial vehicle (UAV) photography images of forestland under hazy conditions, this study proposes a de-fogging method for UAV images adapted to forestland aerial photography scenes based on Resnet. 【Method】 The UAV images in woodland scenes were characterized by texture features and rich high-and low-frequency information. GFF information transfer modules were attached to each layer of the backbone network to transform feature maps into weight maps for filtering and sending to other layers, and thresholds were set at the receiving end to avoid the adverse effects of redundant information. Then, the global defogging effect was enhanced by dense links to improve the defogging quality in high- and low-frequency image regions. Finally, defogging experiments were conducted on a test set of woodland UAV images with fog. 【Result】 The average structural similarity of DHnet on the test set of woodland images was 0.83, and the average peak signal-to-noise ratio was 22.3 dB, which represented improvements of 4.8% and 39.3%, respectively, compared with the Resnet method. 【Conclusion】 The algorithm can effectively reduce tonal shift and remove residual fog, improving the color fidelity and detailed information retention of aerial woodland fog images obtained by UAV photography.