用逻辑门构建轻量图像修复模型，速度更快且效果好
LogicIR: Logic Gate Networks for Image Restoration
- 全由NAND/XOR等逻辑门构成的UNet结构
- 在多个基准上实现高恢复质量，计算量大幅降低
- 适合部署在资源受限设备上的图像修复任务
图像修复旨在从退化的低质量输入中重建高质量图像。随着图像修复模型计算需求持续上升，轻量化架构在快速高效推理方面备受关注。逻辑门网络（LGN）利用NAND、XOR等基本逻辑运算，成为实现高效计算的新兴方向。然而，其在图像修复领域仍处于空白。本文提出LogicIR，首个专为图像修复设计的逻辑门网络。LogicIR采用完全由逻辑门组成的UNet式架构，并引入可微位解码层和索引打乱机制，提升逻辑门间信息传播效率。在多个图像修复基准测试中，LogicIR展现出优异性能，同时计算成本显著降低，验证了其作为高效替代方案的可行性。代码已开源：https://github.com/jimmy9704/LogicIR。
原文摘要 · Abstract (English)
Image restoration aims to reconstruct high-quality images from degraded low-quality inputs. As the computational demands of image restoration models continue to rise, there is growing interest in lightweight architectures optimized for fast and efficient inference. Logic gate networks (LGNs), which operate using fundamental logic operations such as NAND and XOR, have recently emerged as a promising direction for achieving highly efficient computation. However, their potential remains largely untapped in the domain of image restoration. In this work, we introduce LogicIR, the first LGN specifically designed for image restoration tasks. LogicIR incorporates a UNet-inspired architecture composed entirely of logic gates. In addition, we propose a differentiable bit decoding layer and an index shuffling mechanism that improves information propagation across logic gates. Experimental results across multiple image restoration benchmarks demonstrate that LogicIR achieves strong performance with significantly reduced computational cost, establishing LogicIR as a viable and efficient alternative for image restoration. The source code is available at https://github.com/jimmy9704/LogicIR
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