DYNA JOURNAL ENGINEERING

Key words:

Image fusion, Pulse coupled neural network, Salience detection, Convolution sparse representation, Fusión de imágenes, Red neuronal acoplada por pulsos, Detección de saliencia, Representación dispersa por convolución

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

Existing image fusion methods based on pulse coupled neural network (PCNN) ignore the fact that the human brain is essential to the organic combination of neural network and fuzzy system. As a result, some pixels that carry detailed information cannot activate neurons, thereby resulting in loss of details of source images and lowering the quality of fused images. To solve this problem, the present study proposed an infrared and visible image fusion method based on fuzzy pulse coupled neural network (FPCNN) and salience detection. This proposed method constructed FPCNN by combining the fuzzy theory and pulse coupled neural network. The cartoon parts obtained from the decomposition of source images by the total variation model were fused based on FPCNN. Then, salience detection of coefficients of the convolution sparse representation of the texture parts was conducted. The texture parts were fused on the basis of salience detection results. Finally, the fused cartoon and texture parts were added to obtain the fused image. The effectiveness and superiority of the proposed method were verified by experiments. Results demonstrate that compared with the most advanced seven algorithms, the proposed algorithm is superior in terms of objective evaluation indexes such as information entropy, average gradient, structural similarity, and fusion performance based on gradient. Moreover, the proposed method has good visual effect. This study provides a reference for producing high-quality all-weather images in whole-scene description.

Keywords: Image fusion, Pulse coupled neural network, Salience detection, Convolution sparse representation