IEEE Trans Image Process - From local pixel structure to global image super-resolution: a new face hallucination framework.

Tópicos

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Resumo

We have developed a new face hallucination framework termed from local pixel structure to global image super-resolution (LPS-GIS). Based on the assumption that two similar face images should have similar local pixel structures, the new framework first uses the input low-resolution (LR) face image to search a face database for similar example high-resolution (HR) faces in order to learn the local pixel structures for the target HR face. It then uses the input LR face and the learned pixel structures as priors to estimate the target HR face. We present a three-step implementation procedure for the framework. Step 1 searches the database for K example faces that are the most similar to the input, and then warps the K example images to the input using optical flow. Step 2 uses the warped HR version of the K example faces to learn the local pixel structures for the target HR face. An effective method for learning local pixel structures from an individual face, and an adaptive procedure for fusing the local pixel structures of different example faces to reduce the influence of warping errors, have been developed. Step 3 estimates the target HR face by solving a constrained optimization problem by means of an iterative procedure. Experimental results show that our new method can provide good performances for face hallucination, both in terms of reconstruction error and visual quality; and that it is competitive with existing state-of-the-art methods.

Resumo Limpo

develop new face hallucin framework term local pixel structur global imag superresolut lpsgis base assumpt two similar face imag similar local pixel structur new framework first use input lowresolut lr face imag search face databas similar exampl highresolut hr face order learn local pixel structur target hr face use input lr face learn pixel structur prior estim target hr face present threestep implement procedur framework step search databas k exampl face similar input warp k exampl imag input use optic flow step use warp hr version k exampl face learn local pixel structur target hr face effect method learn local pixel structur individu face adapt procedur fuse local pixel structur differ exampl face reduc influenc warp error develop step estim target hr face solv constrain optim problem mean iter procedur experiment result show new method can provid good perform face hallucin term reconstruct error visual qualiti competit exist stateoftheart method

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