共享中间层，让多个图像用更少参数高效编码
MINR: Efficient Implicit Neural Representations for Multi-Image Encoding
- 共享中间层+独立投影层，实现多图高效编码
- 参数减少60%，100张图平均PSNR达34dB
- 适合大规模图像处理与低资源部署场景
隐式神经表示（INRs）通过隐式连续函数参数化离散信号。然而，为每张图像单独使用神经网络（通常为MLP）会导致多图像编码时计算与存储效率低下。为此，本文提出MINR，通过共享特定层实现多图像高效编码。我们首先分析多个训练好的INRs的层间权重分布，发现对应中间层具有高度相似的分布模式。受此启发，我们在多图像间共享这些中间层，同时保留输入与输出层作为输入相关。此外，为每张图像设计一个额外的投影层以捕捉其独特特征。在图像重建与超分辨率任务上的实验表明，MINR可节省高达60%的参数，同时保持相当的性能表现。特别地，MINR能有效扩展至100张图像，平均峰值信噪比（PSNR）维持在34 dB。对多种骨干网络的进一步分析证明了所提MINR的鲁棒性。
原文摘要 · Abstract (English)
Implicit Neural Representations (INRs) aim to parameterize discrete signals through implicit continuous functions. However, formulating each image with a separate neural network~(typically, a Multi-Layer Perceptron (MLP)) leads to computational and storage inefficiencies when encoding multi-images. To address this issue, we propose MINR, sharing specific layers to encode multi-image efficiently. We first compare the layer-wise weight distributions for several trained INRs and find that corresponding intermediate layers follow highly similar distribution patterns. Motivated by this, we share these intermediate layers across multiple images while preserving the input and output layers as input-specific. In addition, we design an extra novel projection layer for each image to capture its unique features. Experimental results on image reconstruction and super-resolution tasks demonstrate that MINR can save up to 60\% parameters while maintaining comparable performance. Particularly, MINR scales effectively to handle 100 images, maintaining an average peak signal-to-noise ratio (PSNR) of 34 dB. Further analysis of various backbones proves the robustness of the proposed MINR.
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