Image super resolution refers to a class of signal processing algorithms to post-process a captured image to obtain its high resolution version. Multi-frame super resolution synthesises high resolution image from multiple low resolution observations. Performance of super resolution algorithms are adversely affected by the noise present in the input images. To develop a noise robust multi-frame image super resolution, an objective function is formulated which contains a weighted data fidelity term and a regularisation term consisting of a bilateral total variation (BTV) term and structure tensor total variation (STV) term. Both BTV and STV are weighted appropriately in a per pixel basis in such a way that the BTV contributes more in smooth regions and STV contributes more on the edges. These terms ensure the continuity of edges and the smoothness of flat regions. An adaptive weighting scheme with the data fidelity term helps to select the reliable pixel alone in the reconstruction process. The proposed method is experimentally evaluated for its performance in real data and different types of noises.

中文翻译：

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使用空间加权总变化正则化的多帧图像超分辨率

图像超分辨率是指对捕获的图像进行后处理以获得其高分辨率版本的一类信号处理算法。多帧超分辨率可从多个低分辨率观察值中合成高分辨率图像。输入图像中存在的噪声会对超分辨率算法的性能产生不利影响。为了开发具有噪声鲁棒性的多帧图像超分辨率，制定了一个目标函数，该目标函数包含一个加权数据保真度项和一个正则化项，该正则化项包括双边总变化量（BTV）项和结构张量总变化量（STV）项。BTV和STV均以每个像素为基础进行适当加权，以使BTV在平滑区域中的贡献更大，而STV在边缘上的贡献更大。这些术语确保边缘的连续性和平坦区域的平滑度。具有数据保真度项的自适应加权方案有助于在重建过程中单独选择可靠的像素。通过实验评估了该方法在真实数据和不同类型噪声中的性能。