轻量级去噪模型，能自动适应不同噪声水平
Small, Bias-Free, Blind and Convolutional Denoiser: A compact ConvNeXt U-Net for blind Gaussian color-image denoising
- 采用无偏结构与Gabor茎干，实现输入缩放不变性
- 单模型在噪声0~200下平滑降噪，200时仍达20.0 dB
- 参数仅0.82M，性能接近大模型，适合移动端部署
本文提出BF-ConvUNeXt，一种用于盲加性白高斯噪声彩色图像去噪的紧凑无偏ConvNeXt U-Net。其结合四项设计：冻结深度可分离Gabor茎干（零训练参数）、拉普拉斯金字塔编码器将高频残差注入各跳连、ConvNeXt-V1 U-Net主干，以及全程无偏架构（无加性偏置、线性输出头、LeakyReLU、仅方差批归一化）。该设计使0.82M参数网络在推理时保持严格一次齐次性，满足D(alpha y) = alpha D(y)，支持基于Miyasawa/Tweedie的残差评分，实现单一模型跨噪声水平的盲泛化。模型在噪声标准差6.4至64（0-255尺度）的课程训练下，外推至σ=150时仍保持22.8 dB，σ=200时为20.0 dB。在CBSD68、Kodak24、McMaster、Urban100四个标准数据集上，σ∈{15,25,50}时表现优于或匹配DnCNN和FFDNet，平均领先约+0.7 dB；相比主流重型CNN/Transformer模型，仅落后0.3-1.7 dB，但参数仅为后者的1/15至1/39。齐次性仅限推理与特定检查点，学习残差为局部而非全局得分（非保守雅可比），故不支持即插即用/RED保证，但仍可用于随机采样及线性逆问题（如修复、超分、去模糊、压缩感知）。
原文摘要 · Abstract (English)
We describe and evaluate BF-ConvUNeXt, a compact bias-free ConvNeXt U-Net for blind additive-white-Gaussian-noise color image denoising, combining four existing ingredients so a single property survives end to end: a frozen depthwise Gabor stem (oriented band-pass, zero trainable parameters), a Laplacian-pyramid encoder routing the high-frequency residual into each skip connection, a ConvNeXt-V1 U-Net body, and bias-free construction throughout (no additive bias, linear head, LeakyReLU, variance-only batch norm). Together these make the 0.82M-parameter network exactly degree-1 homogeneous at inference, D(alpha y) = alpha D(y), licensing a Miyasawa/Tweedie score reading of the residual and blind generalization across noise levels from one model. We train a single blind model on a noise-sigma curriculum (sigma approximately 6.4 to 64, 0-255 scale); it extrapolates past that ceiling without a cliff, degrading smoothly to 22.8 dB at sigma=150 and 20.0 dB at sigma=200. Evaluated unchanged on four standard color sets (CBSD68, Kodak24, McMaster, Urban100) at sigma in {15,25,50}, it matches or beats DnCNN and FFDNet on every set and level, averaging about +0.7 dB over DnCNN. Against heavyweight CNN/transformer state of the art it trails by a small margin (roughly 0.3-1.7 dB depending on set) at 1/15 to 1/39 of their parameters. The homogeneity is inference-only and checkpoint-specific, and the learned residual is a local, not global, score (non-conservative Jacobian), so plug-and-play/RED guarantees do not transfer; it still drives stochastic sampling and linear inverse problems (inpainting, super-resolution, deblurring, compressive sensing).
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