IEEE Trans Image Process - Face super-resolution via multilayer locality-constrained iterative neighbor embedding and intermediate dictionary learning.

Tópicos

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Resumo

Based on the assumption that low-resolution (LR) and high-resolution (HR) manifolds are locally isometric, the neighbor embedding super-resolution algorithms try to preserve the geometry (reconstruction weights) of the LR space for the reconstructed HR space, but neglect the geometry of the original HR space. Due to the degradation process of the LR image (e.g., noisy, blurred, and down-sampled), the neighborhood relationship of the LR space cannot reflect the truth. To this end, this paper proposes a coarse-to-fine face super-resolution approach via a multilayer locality-constrained iterative neighbor embedding technique, which intends to represent the input LR patch while preserving the geometry of original HR space. In particular, we iteratively update the LR patch representation and the estimated HR patch, and meanwhile an intermediate dictionary learning scheme is employed to bridge the LR manifold and original HR manifold. The proposed method can faithfully capture the intrinsic image degradation shift and enhance the consistency between the reconstructed HR manifold and the original HR manifold. Experiments with application to face super-resolution on the CAS-PEAL-R1 database and real-world images demonstrate the power of the proposed algorithm.

Resumo Limpo

base assumpt lowresolut lr highresolut hr manifold local isometr neighbor embed superresolut algorithm tri preserv geometri reconstruct weight lr space reconstruct hr space neglect geometri origin hr space due degrad process lr imag eg noisi blur downsampl neighborhood relationship lr space reflect truth end paper propos coarsetofin face superresolut approach via multilay localityconstrain iter neighbor embed techniqu intend repres input lr patch preserv geometri origin hr space particular iter updat lr patch represent estim hr patch meanwhil intermedi dictionari learn scheme employ bridg lr manifold origin hr manifold propos method can faith captur intrins imag degrad shift enhanc consist reconstruct hr manifold origin hr manifold experi applic face superresolut caspealr databas realworld imag demonstr power propos algorithm

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