Reciprocal Attention Mixing Transformer for Lightweight Image Restoration
arxiv(2023)
摘要
Although many recent works have made advancements in the image restoration
(IR) field, they often suffer from an excessive number of parameters. Another
issue is that most Transformer-based IR methods focus only on either local or
global features, leading to limited receptive fields or deficient parameter
issues. To address these problems, we propose a lightweight IR network,
Reciprocal Attention Mixing Transformer (RAMiT). It employs our proposed
dimensional reciprocal attention mixing Transformer (D-RAMiT) blocks, which
compute bi-dimensional (spatial and channel) self-attentions in parallel with
different numbers of multi-heads. The bi-dimensional attentions help each other
to complement their counterpart's drawbacks and are then mixed. Additionally,
we introduce a hierarchical reciprocal attention mixing (H-RAMi) layer that
compensates for pixel-level information losses and utilizes semantic
information while maintaining an efficient hierarchical structure. Furthermore,
we revisit and modify MobileNet V1 and V2 to attach efficient convolutions to
our proposed components. The experimental results demonstrate that RAMiT
achieves state-of-the-art performance on multiple lightweight IR tasks,
including super-resolution, color denoising, grayscale denoising, low-light
enhancement, and deraining. Codes are available at
https://github.com/rami0205/RAMiT.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要