PixelShuffler：一种通过像素重排实现的简单图像翻译方法
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
AI总结：
本文提出PixelShuffler像素混洗方法，通过最大化风格图像像素重排后与内容图像的互信息实现风格迁移，在LPIPS和FID指标上达到与复杂最先进方法相当的性能，同时显著降低计算复杂度。
摘要：
图像到图像翻译是计算机视觉中的一个课题，具有广泛的应用场景，包括医学图像翻译（如将MRI扫描转换为CT扫描或其他MRI对比度）、图像着色、超分辨率、域适应以及从草图或语义图生成逼真图像。图像风格迁移也是图像到图像翻译中被广泛研究的应用之一，其目标是合成一幅同时结合一幅图像的内容与另一幅图像风格的图像。现有最先进的方法通常依赖复杂的神经网络，包括扩散模型和语言模型，以实现高质量的风格迁移，但这些方法计算成本高且实现复杂。在本文中，我们提出了一种新颖的像素混洗方法，以风格迁移作为具体示范应用，从一般意义上解决图像到图像翻译问题。所提出的方法通过混洗风格图像的像素，使得混洗后的图像与内容图像之间的互信息最大化，从而实现风格迁移。该方法本质上保留了风格图像的颜色，同时确保内容图像的结构细节在风格化输出中得到保留。我们证明，这种简单直接的方法产生的结果可与最先进技术相媲美，分别以用于内容保留的Learned Perceptual Image Patch Similarity (LPIPS)损失和用于风格相似性的Fréchet Inception Distance (FID)分数来衡量。我们的实验验证了所提出的像素混洗方法在显著降低复杂度的情况下取得了具有竞争力的性能，为高效图像风格迁移提供了一种有前景的替代方案，同时也显示了该方法在一般图像到图像翻译任务中的可用性前景。
英文摘要：
Image-to-image translation is a topic in computer vision that has a vast range of use cases ranging from medical image translation, such as converting MRI scans to CT scans or to other MRI contrasts, to image colorization, super-resolution, domain adaptation, and generating photorealistic images from sketches or semantic maps. Image style transfer is also a widely researched application of image-to-image translation, where the goal is to synthesize an image that combines the content of one image with the style of another. Existing state-of-the-art methods often rely on complex neural networks, including diffusion models and language models, to achieve high-quality style transfer, but these methods can be computationally expensive and intricate to implement. In this paper, we propose a novel pixel shuffle method that addresses the image-to-image translation problem generally with a specific demonstrative application in style transfer. The proposed method approaches style transfer by shuffling the pixels of the style image such that the mutual information between the shuffled image and the content image is maximized. This approach inherently preserves the colors of the style image while ensuring that the structural details of the content image are retained in the stylized output. We demonstrate that this simple and straightforward method produces results that are comparable to state-of-the-art techniques, as measured by the Learned Perceptual Image Patch Similarity (LPIPS) loss for content preservation and the Fréchet Inception Distance (FID) score for style similarity. Our experiments validate that the proposed pixel shuffle method achieves competitive performance with significantly reduced complexity, offering a promising alternative for efficient image style transfer, as well as a promise in usability of the method in general image-to-image translation tasks.
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