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Convolutional neural network (CNN) based image enhancement methods such assuper-resolution and detail enhancement have achieved remarkable performances.However, amounts of operations including convolution and parameters within thenetworks cost high computing power and need huge memory resource, which limitsthe applications with on-device requirements. Lightweight image enhancementnetwork should restore details, texture, and structural information fromlow-resolution input images while keeping their fidelity. To address theseissues, a lightweight image enhancement network is proposed. The proposednetwork include self-feature extraction module which produces modulationparameters from low-quality image itself, and provides them to modulate thefeatures in the network. Also, dense modulation block is proposed for unitblock of the proposed network, which uses dense connections of concatenatedfeatures applied in modulation layers. Experimental results demonstrate betterperformance over existing approaches in terms of both quantitative andqualitative evaluations.

Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation