Barbu Adrian
Department of Statistics, Florida State University, Tallahassee, FL 32306, USA.
IEEE Trans Image Process. 2009 Nov;18(11):2451-62. doi: 10.1109/TIP.2009.2028254. Epub 2009 Jul 24.
Many computer vision problems can be formulated in a Bayesian framework based on Markov random fields (MRF) or conditional random fields (CRF). Generally, the MRF/CRF model is learned independently of the inference algorithm that is used to obtain the final result. In this paper, we observe considerable gains in speed and accuracy by training the MRF/CRF model together with a fast and suboptimal inference algorithm. An active random field (ARF) is defined as a combination of a MRF/CRF based model and a fast inference algorithm for the MRF/CRF model. This combination is trained through an optimization of a loss function and a training set consisting of pairs of input images and desired outputs. We apply the ARF concept to image denoising, using the Fields of Experts MRF together with a 1-4 iteration gradient descent algorithm for inference. Experimental validation on unseen data shows that the ARF approach obtains an improved benchmark performance as well as a 1000-3000 times speedup compared to the Fields of Experts MRF. Using the ARF approach, image denoising can be performed in real-time, at 8fps on a single CPU for a 256 x 256 image sequence, with close to state-of-the-art accuracy.
许多计算机视觉问题可以基于马尔可夫随机场（MRF）或条件随机场（CRF）在贝叶斯框架中进行表述。通常，MRF/CRF模型的学习独立于用于获得最终结果的推理算法。在本文中，我们观察到通过将MRF/CRF模型与一种快速且次优的推理算法一起训练，在速度和准确性方面有显著提升。主动随机场（ARF）被定义为基于MRF/CRF的模型与用于MRF/CRF模型的快速推理算法的组合。这种组合通过对损失函数和由输入图像对与期望输出组成的训练集进行优化来训练。我们将ARF概念应用于图像去噪，使用专家场MRF以及用于推理的1 - 4次迭代梯度下降算法。对未见数据的实验验证表明，与专家场MRF相比，ARF方法获得了改进的基准性能以及1000 - 3000倍的加速。使用ARF方法，可以在单个CPU上以8fps的速度对256×256图像序列实时执行图像去噪，且精度接近当前最优水平。
