Fields of experts (FoE) image denoising is one of the most promising high-order Markov random field (MRF)-based image denoising methods. However, the original algorithm by Roth and Black did not consider the parameter selection problem in its iteration, so it cannot be directly applied to real image denoising tasks. An automatic stopping criterion in FoE image denoising is introduced, through which the denoised image can be obtained without reference image and noise variance estimation. Experimental results validate its better performance than the classic FoE method, both on synthetic and real noisy images.
Superresolution reconstruction produces a high-resolution image from a set of low-resolution images. Accurate subpixel image registration is critical in image superresolution reconstruction. The existence of outliers, which are defined as data points with different distributional characteristics from the assumed model, will produce erroneous image registration estimates that lead to undesirable results. Several solutions have been proposed to handle registration errors as a part of the regularized solution in the reconstruction step; however, they are invalid for videos that contain localized outliers, such as moving objects in the frames. We present a new robust image superresolution method to handle the localized motion outliers. We first separate the low-resolution image into several layers. After identifying the motion models of the layers, we calculate these separately. Then, we can obtain an accurate subpixel image registration of the background that contains important information. Finally, we fuse them into a high-resolution image. The effectiveness of our model is demonstrated with results from superresolution experiments with both synthetic and real sequences in the presence of localized motion outliers.