Efficient feature redundancy reduction for image denoising

Abstract

It is challenging to deploy convolutional neural networks (CNNs) for image denoising on low-power devices which can suffer from computational and memory constraints. To address this limitation, a simple yet effective and efficient feature redundancy reduction-based network (FRRN) is proposed in this paper, which integrates a feature refinement block (FRB), an attention fusion block (AFB), and an enhancement block (EB). Specifically, the FRB distills structural information via two parallel sub-networks, selecting representative feature representations while suppressing spatial-channel redundancy. The AFB absorbs an attentive fusion mechanism to facilitate diverse features extracted from two sub-networks, emphasizing texture and structure details but alleviating harmful features from problematic regions. The subsequent EB further boosts the feature representation abilities. Aiming to enhance denoising performance at both pixel level and semantic level, a multi-loss scheme comprising three popular loss functions is leveraged to improve the robustness of the denoiser. Comprehensive quantitative and qualitative analyses demonstrate the superiority of the proposed FRRN.