用波段抑制替代传统上采样，提升细节恢复并降低计算开销。
SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders
- 提出波段抑制机制，以频率域投影代替空间上采样。
- 在深度估计中边缘AUC提升至34.45%，计算量减少66%。
- 可直接替换现有U-Net解码器，适合需高细节与低功耗场景。
跳接式U-Net变体广泛用于密集逆问题，但其解码器通常通过空间上采样恢复分辨率，易模糊或扭曲细结构。小波变换提供显式的无损重构路径，但先前的小波网络常将此特性绑定于特定架构或固定输出表示。本文提出选择性抑制的完美重建（SUPER），一种基于小波域的解码器设计，用有界频率抑制取代无结构的空间细化。理想等通道形式下，当抑制项为零时，可实现完全重建；实际U-Net解码器中，提供可学习的任务相关投影子空间重构。所提SUPER模块为即插即用的解码块：替换现有U-Net类解码器中的上采样/优化阶段，保持编码器与任务头不变。在单目深度估计、细裂缝分割和手机图像去噪任务上评估。iDisc深度估计中，边缘AUC从26.59%提升至34.45%，解码器算力从12.69G降至4.36G；FACS-Net裂缝分割中，平均IoU及0-2像素极细裂缝区域表现均提升；SIDD去噪任务中，虽高频增强不直接奖励，仍保持PSNR/SSIM，解码器算力降低66.6%。结果表明SUPER是改善U-Net类解码器细节-成本权衡的实用小波域抑制模块。
原文摘要 · Abstract (English)
Skip-connected U-Net variants are widely used for dense inverse problems, yet their decoders commonly recover resolution through spatial upscaling, which can blur or distort fine structures. Wavelet transforms provide an explicit perfect-reconstruction path, but prior wavelet networks often couple this property to wavelet-specific architectures or fixed output representations. We propose Selectively Suppressed Perfect Reconstruction (SUPER), a wavelet-domain decoder formulation that replaces unstructured spatial refinement with bounded frequency suppression. In its idealized equal-channel form, SUPER reduces to exact reconstruction when the suppression term is zero; in practical U-Net decoders, it provides a projected-subspace reconstruction fallback with learnable task-specific suppression. The resulting SUPER Module is a structurally plug-in decoder block: it replaces the upsampling/refinement stage of an existing U-Net-like decoder, while leaving the encoder and task head unchanged. We evaluate SUPER on monocular depth estimation, thin-crack segmentation, and smartphone image denoising. On iDisc depth estimation, SUPER improves the host model's edge AUC from 26.59% to 34.45% while reducing decoder MACs from 12.69G to 4.36G. On FACS-Net crack segmentation, SUPER improves average IoU and the extreme 0-2px crack regime. On SIDD denoising, where high-frequency enhancement is less directly rewarded, SUPER preserves PSNR/SSIM while reducing decoder MACs by 66.6%. These results support SUPER as a practical wavelet-domain suppression module for improving the observed detail-cost trade-off of U-Net-like decoders.
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