Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation
This work addresses the need for efficient image enhancement on mobile devices, representing an incremental improvement in lightweight network design.
The paper tackles the problem of high computational cost and memory usage in CNN-based image enhancement for mobile devices by proposing a lightweight network with self-feature extraction and dense modulation, achieving better performance than existing methods in quantitative and qualitative evaluations.
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.