IEEE Trans Image Process - Image super-resolution via sparse representation.

Tópicos

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Resumo

This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low resolution and high resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low resolution image patch can be applied with the high resolution image patch dictionary to generate a high resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs, reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle super-resolution with noisy inputs in a more unified framework.

Resumo Limpo

paper present new approach singleimag superresolut base spars signal represent research imag statist suggest imag patch can wellrepres spars linear combin element appropri chosen overcomplet dictionari inspir observ seek spars represent patch lowresolut input use coeffici represent generat highresolut output theoret result compress sens suggest mild condit spars represent can correct recov downsampl signal joint train two dictionari low highresolut imag patch can enforc similar spars represent low resolut high resolut imag patch pair respect dictionari therefor spars represent low resolut imag patch can appli high resolut imag patch dictionari generat high resolut imag patch learn dictionari pair compact represent patch pair compar previous approach simpli sampl larg amount imag patch pair reduc comput cost substanti effect sparsiti prior demonstr general imag superresolut special case face hallucin case algorithm generat highresolut imag competit even superior qualiti imag produc similar sr method addit local spars model approach natur robust nois therefor propos algorithm can handl superresolut noisi input unifi framework

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