Abstract
High-resolution imagery is often hindered by limitations in sensor technology, atmospheric conditions, and costs. Such challenges occur in satellite remote sensing, but also with handheld cameras, such as our smartphones. Hence, super-resolution aims to enhance the image resolution algorithmically. Since single-image super-resolution requires solving an inverse problem, such methods must exploit strong priors, e.g. learned from high-resolution training data, or be constrained by auxiliary data, e.g. by a high-resolution guide from another modality. While qualitatively pleasing, such approaches often lead to "hallucinated" structures that do not match reality. In contrast, multi-image super-resolution (MISR) aims to improve the (optical) resolution by constraining the super-resolution process with multiple views taken with sub-pixel shifts. Here, we propose SuperF, a test-time optimization approach for MISR that leverages coordinate-based neural networks, also called neural fields. Their ability to represent continuous signals with an implicit neural representation (INR) makes them an ideal fit for the MISR task. The key characteristic of our approach is to share an INR for multiple shifted low-resolution frames and to jointly optimize the frame alignment with the INR. Our approach advances related INR baselines, adopted from burst fusion for layer separation, by directly parameterizing the sub-pixel alignment as optimizable affine transformation parameters and by optimizing via a super-sampled coordinate grid that corresponds to the output resolution. Our experiments yield compelling results on simulated bursts of satellite imagery and ground-level images from handheld cameras, with upsampling factors of up to 8. A key advantage of SuperF is that this approach does not rely on any high-resolution training data.
Abstract (translated)
高分辨率成像经常受到传感器技术、大气条件和成本的限制。这些挑战不仅存在于卫星遥感中，也出现在手持设备如智能手机摄像头拍摄的照片上。因此，超分辨率旨在通过算法增强图像的清晰度。单幅图像的超分辨率需要解决一个逆向问题，这类方法必须利用强大的先验信息，例如从高分辨率训练数据中学到的信息，或者受到辅助数据（例如其他模态提供的高分辨率指南）的限制。尽管这些方法在视觉上令人满意，但它们往往会产生与现实不符的“幻觉”结构。相比之下，多图像超分辨率（MISR）的目标是通过使用具有亚像素偏移的多个视图来提高（光学）分辨率。在这里，我们提出了一种名为SuperF的方法，这是一种针对MISR的测试时优化方法，它利用了坐标基神经网络或称为神经场的模型。由于它们能够用隐式神经表示（INR）连续信号的能力，使其非常适合于MISR任务。我们的方法的关键特征是为多个偏移低分辨率帧共享一个INR，并联合优化帧对齐与INR的过程。相较于从层分离的突发融合中采用的相关INR基准线，SuperF直接参数化亚像素对准作为可优化仿射变换参数，并通过对应于输出分辨率的超采样坐标网格进行优化。我们在模拟卫星图像和手持相机拍摄的地面级图像的爆发数据上进行了实验，实现了高达8倍的上采样率。SuperF的一个关键优势是这种方法不依赖任何高分辨率训练数据。
URL
https://arxiv.org/abs/2512.09115
