IEEE Trans Image Process - Generalized random walks for fusion of multi-exposure images.

Tópicos

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Resumo

A single captured image of a real-world scene is usually insufficient to reveal all the details due to under- or over-exposed regions. To solve this problem, images of the same scene can be first captured under different exposure settings and then combined into a single image using image fusion techniques. In this paper, we propose a novel probabilistic model-based fusion technique for multi-exposure images. Unlike previous multi-exposure fusion methods, our method aims to achieve an optimal balance between two quality measures, i.e., local contrast and color consistency, while combining the scene details revealed under different exposures. A generalized random walks framework is proposed to calculate a globally optimal solution subject to the two quality measures by formulating the fusion problem as probability estimation. Experiments demonstrate that our algorithm generates high-quality images at low computational cost. Comparisons with a number of other techniques show that our method generates better results in most cases.

Resumo Limpo

singl captur imag realworld scene usual insuffici reveal detail due overexpos region solv problem imag scene can first captur differ exposur set combin singl imag use imag fusion techniqu paper propos novel probabilist modelbas fusion techniqu multiexposur imag unlik previous multiexposur fusion method method aim achiev optim balanc two qualiti measur ie local contrast color consist combin scene detail reveal differ exposur general random walk framework propos calcul global optim solut subject two qualiti measur formul fusion problem probabl estim experi demonstr algorithm generat highqual imag low comput cost comparison number techniqu show method generat better result case

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