IEEE Trans Image Process - Scale Adaptive Dictionary Learning.

Tópicos

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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Dictionary learning has been widely used in many image processing tasks. In most of these methods, the number of basis vectors is either set by experience or coarsely evaluated empirically. In this paper, we propose a new scale adaptive dictionary learning framework, which jointly estimates suitable scales and corresponding atoms in an adaptive fashion according to the training data, without the need of prior information. We design an atom counting function and develop a reliable numerical scheme to solve the challenging optimization problem. Extensive experiments on texture and video data sets demonstrate quantitatively and visually that our method can estimate the scale, without damaging the sparse reconstruction ability.

Resumo Limpo

dictionari learn wide use mani imag process task method number basi vector either set experi coars evalu empir paper propos new scale adapt dictionari learn framework joint estim suitabl scale correspond atom adapt fashion accord train data without need prior inform design atom count function develop reliabl numer scheme solv challeng optim problem extens experi textur video data set demonstr quantit visual method can estim scale without damag spars reconstruct abil

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