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Learning-based face super-resolution approaches rely on representative dictionary as self-similarity prior from training samples to estimate the relationship between the low-resolution (LR) and high-resolution (HR) image patches. The most popular approaches, learn mapping function directly from LR patches to HR ones but neglects the multi-layered nature of image degradation process (resolution down-sampling)...
Patch-based face hallucination algorithms utilize either local patches (e.g., position-patch approaches) or nonlocal patches (e.g., dictionary-learning approaches) to exploit self-similarity prior from training samples. Although they yield decent results, solo source patches limit their performance due to not fully taking self-similarity prior from both local and nonlocal ones. In order to overcome...
Learning-based face super-resolution relies on obtaining accurate a priori knowledge from the training data. Representation-based approaches (e.g., sparse representation-based and neighbor embedding-based schemes) decompose the input images using sophisticated regularization techniques. They give reasonably good reconstruction performance. However, in real application scenarios, the input images are...
In this paper, we present an automatic face replacement approach in photographs based on Active Shape Models (ASM). Our replacement algorithm has three main modules: face alignment, face morph, and seamless blending. First, given an input and target image, we automatically implement face alignment using ASM. Second, we fit a morphable 2D model to the input face, and warp its shape to match the target...
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