论文标题
使用自我提取和密集调制的移动设备的轻量级图像增强网络
Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation
论文作者
论文摘要
基于卷积神经网络（CNN）的图像增强方法（例如超分辨率和细节增强）已取得了出色的性能。但是，包括网络中的卷积和参数在内的操作量占了较高的计算能力，并且需要巨大的内存资源，这限制了应用程序要求的应用程序。轻巧的图像增强网络应从低分辨率输入图像中恢复细节，纹理和结构信息，同时保持其保真度。为了解决这些问题，提出了轻巧的图像增强网络。所提出的网络包括自我提取模块，该模块从低质量图像本身产生调制参数，并提供了调节网络中的功能。同样，提出了针对拟议网络的单位块的密集调制块，该单位块使用在调制层中应用的串联特征的密集连接。实验结果表明，就定量和定性评估而言，现有方法的性能更好。
Convolutional neural network (CNN) based image enhancement methods such as super-resolution and detail enhancement have achieved remarkable performances. However, amounts of operations including convolution and parameters within the networks cost high computing power and need huge memory resource, which limits the applications with on-device requirements. Lightweight image enhancement network should restore details, texture, and structural information from low-resolution input images while keeping their fidelity. To address these issues, a lightweight image enhancement network is proposed. The proposed network include self-feature extraction module which produces modulation parameters from low-quality image itself, and provides them to modulate the features in the network. Also, dense modulation block is proposed for unit block of the proposed network, which uses dense connections of concatenated features applied in modulation layers. Experimental results demonstrate better performance over existing approaches in terms of both quantitative and qualitative evaluations.
