To overcome the limitations of the traditional fields of experts (FoE) model, which will blur image edges and texture during the denoising processing, a spatial information weighted FoE (WFoE) model has been presented to introduce the image spatial structure information into the FoE model. A monotone decreasing function is based on the curvature difference to control the filter weight in the edge and smooth region. The proposed WFoE model can better remove noise while preserving edges. Additionally, the proposed WFoE model is designed as a regularization term in the maximum a posteriori-based multi-frame image super-resolution (SR) reconstruction algorithm, enabling the development of a new SR method. Since the WFoE model is more inclined to keep image edges, the proposed WFoE-based SR reconstruction method can obtain better results than traditional FoE model with respect to preserving image edges. Experimental results demonstrate that our method has better peak signal-to-noise ratio and visual verisimilitude compared with some existing SR methods.

中文翻译：

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基于专家空间信息加权场的多帧图像超分辨率重建

为了克服传统专家领域（FoE）模型在去噪处理过程中会模糊图像边缘和纹理的局限性，提出了一种空间信息加权FoE（WFoE）模型，将图像空间结构信息引入到FoE模型中。 . 单调递减函数基于曲率差来控制边缘和平滑区域的滤波器权重。所提出的 WFoE 模型可以在保留边缘的同时更好地去除噪声。此外，所提出的 WFoE 模型被设计为基于最大后验的多帧图像超分辨率 (SR) 重建算法中的正则化项，从而能够开发新的 SR 方法。由于 WFoE 模型更倾向于保留图像边缘，所提出的基于 WFoE 的 SR 重建方法在保留图像边缘方面可以获得比传统 FoE 模型更好的结果。实验结果表明，与一些现有的 SR 方法相比，我们的方法具有更好的峰值信噪比和视觉逼真度。