金字塔渐进融合低照度图像增强网络

针对现有低照度图像增强网络对不同尺度特征信息存在感知与表达能力不足的问题,提出金字塔渐进融合低照度图像增强网络模型。网络对图像进行多次下采样操作以组成特征金字塔,通过在特征金字塔的三个不同分支上加入跳跃连接,将不同尺度的特征图进行相互融合。通过精细恢复模块进一步提取精炼信息,将特征图恢复到正常的光照图像。结果表明,该网络模型不但能有效地提升低照度图像的整体亮度,而且能很好地保持图像中的细节信息和清晰的物体边缘轮廓,同时能够有效地抑制图像中的暗部噪声,使增强后的图像整体画面真实自然。

Pyramid asymptotic fusion low-illumination image enhancement network

Since existing low-illumination image enhancement networks have insufficient ability to perceive and express feature information of different scales, a low-illumination image enhancement network model based on pyramid asymptotic fusion was proposed. The network performed multiple down-sampling operations on the image to form a feature pyramid. It fused the feature maps at different scales by adding skip connections to three different branches of the feature pyramid. Fine recovery module further extracted the refined information, and restored the feature map to a normal light image. Results indicate that, the network model not only effectively enhances the brightness of the overall low-illumination image, but also maintains the detailed information and clear edge contours of the objects in the image. Moreover, it can effectively suppress the dark noise, and make the overall enhanced image realistic and natural.