Department of Electronics and Communication, BVB College of Engineering and Technology, Vidyanagar, Hubli 580031, India.
IEEE Trans Pattern Anal Mach Intell. 2011 May;33(5):995-1008. doi: 10.1109/TPAMI.2010.167.
We address the problem of super-resolution—obtaining high-resolution images and videos from multiple low-resolution inputs. The increased resolution can be in spatial or temporal dimensions, or even in both. We present a unified framework which uses a generative model of the imaging process and can address spatial super-resolution, space-time super-resolution, image deconvolution, single-image expansion, removal of noise, and image restoration. We model a high-resolution image or video as a Markov random field and use maximum a posteriori estimate as the final solution using graph-cut optimization technique. We derive insights into what super-resolution magnification factors are possible and the conditions necessary for super-resolution. We demonstrate spatial super-resolution reconstruction results with magnifications higher than predicted limits of magnification. We also formulate a scheme for selective super-resolution reconstruction of videos to obtain simultaneous increase of resolutions in both spatial and temporal directions. We show that it is possible to achieve space-time magnification factors beyond what has been suggested in the literature by selectively applying super-resolution constraints. We present results on both synthetic and real input sequences.
我们解决了超分辨率问题——从多个低分辨率输入中获取高分辨率的图像和视频。增加的分辨率可以在空间或时间维度上，甚至在两者上。我们提出了一个统一的框架，该框架使用成像过程的生成模型，可以解决空间超分辨率、时空超分辨率、图像去卷积、单图像扩展、噪声去除和图像恢复等问题。我们将高分辨率图像或视频建模为马尔可夫随机场，并使用最大后验估计作为最终解决方案，使用图割优化技术。我们深入了解可能的超分辨率放大因子以及超分辨率的必要条件。我们展示了空间超分辨率重建结果，放大倍数高于预测的放大倍数限制。我们还提出了一种用于选择性时空超分辨率重建的方案，以同时提高空间和时间方向的分辨率。我们表明，通过选择性地应用超分辨率约束，可以实现超出文献中建议的时空放大因子。我们在合成和真实输入序列上都展示了结果。
