论文标题
ELMFORMER：有效的原始图像恢复与局部乘法变压器
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformer
论文作者
论文摘要
为了获得下游图像信号过程（ISP）的高质量的原始图像，在本文中，我们提出了一个有效的本地乘法变压器，称为ELMFORMER，用于原始图像恢复。 Elmformer包含两个核心设计，尤其是针对原始属性是单渠道的原始图像。第一个设计是双向融合投影（BFP）模块，我们考虑了原始图像的颜色特征和单渠道的空间结构。第二个是我们提出了一个本地乘法自我注意力（L-MSA）方案，以有效地从当地空间传递信息到相关部分。 Elmformer可以有效地减少计算消耗，并在原始图像恢复任务上表现良好。通过这两种核心设计，Elmformer提高了最高的性能，并且与最先进的制图相比，原始DeNoising和原始Deblurring基准测试最低。广泛的实验证明了Elmformer的优越性和概括能力。在SIDD基准测试中，我们的方法比基于ISP的方法具有更好的降级性能，这些方法需要大量的额外的SRGB培训图像。这些代码在https://github.com/leonmakise/elmformer上发布。
In order to get raw images of high quality for downstream Image Signal Process (ISP), in this paper we present an Efficient Locally Multiplicative Transformer called ELMformer for raw image restoration. ELMformer contains two core designs especially for raw images whose primitive attribute is single-channel. The first design is a Bi-directional Fusion Projection (BFP) module, where we consider both the color characteristics of raw images and spatial structure of single-channel. The second one is that we propose a Locally Multiplicative Self-Attention (L-MSA) scheme to effectively deliver information from the local space to relevant parts. ELMformer can efficiently reduce the computational consumption and perform well on raw image restoration tasks. Enhanced by these two core designs, ELMformer achieves the highest performance and keeps the lowest FLOPs on raw denoising and raw deblurring benchmarks compared with state-of-the-arts. Extensive experiments demonstrate the superiority and generalization ability of ELMformer. On SIDD benchmark, our method has even better denoising performance than ISP-based methods which need huge amount of additional sRGB training images. The codes are release at https://github.com/leonmakise/ELMformer.
