Multi-focus image fusion algorithm based on focused region extraction

Zhang, Baohua; Lu, Xiaoqi; Pei, Haiquan; He, Lifeng; Zhao, Ying; Zhou, Wentao

doi:10.1016/j.neucom.2015.09.092

Cited by 43 publications

(10 citation statements)

References 41 publications

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“…wherein multi-scale transform-based (MST-based) image fusion algorithms are one of the most popular choices [15]. Various MST-based fusion methods have been discussed over the years, ranging from the early wavelet [16] and pyramid [17] transforms to the recently developed multi-scale geometric analysis approaches, such as curvelet [18], contourlet [19], and shearlet [20].…”

Section: Related Workmentioning

confidence: 99%

Robust sparse representation based multi-focus image fusion with dictionary construction and local spatial consistency

Zhang

Shi

Wang

et al. 2018

Pattern Recognition

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Section: Related Workmentioning

confidence: 99%

Robust sparse representation based multi-focus image fusion with dictionary construction and local spatial consistency

Zhang

Shi

Wang

et al. 2018

Pattern Recognition

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“…Uno de los trabajos publicados recientemente por Zhan et al platea un método de solución para el problema de fusión de imágenes multi-foco (Zhang et al, 2016a) y lo compara con los métodos propuestos por (Chai et al, 2011), (Li et al, 2013a), (Duan et al, 2014), (Luo et al, 2012). A pesar de reportar mejores resultados el tiempo de ejecución es mayor.…”

Section: Trabajos Basados En Mapas De Decisiónunclassified

Fusión de Imágenes Multi-Foco con Ventanas Variables

Calderón

Garnica-Carrillo

Flores

2018

Rev. iberoam. autom. inform. ind.

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ResumenEn este artículo presentamos el Algoritmo Combinación Lineal de Imágenes con Ventanas Variables (CLI-VV) para la fusión de imágenes multi-foco. A diferencia del Algoritmo CLI-S presentado en un trabajo anterior, el algoritmo CLI-VV permite determinar automáticamente el tamañoóptimo de la ventana en cada píxel para la segmentación de las regiones con la mayor nitidez. También presentamos la generalizado el Algoritmo CLI-VV para la fusión de conjuntos de imágenes multi-foco con más de dos imágenes. A este nuevo algoritmo lo denominamos Fusión Multi-foco con Ventanas Variables (FM-VV). El Algoritmo CLI-VV se probó con 21 pares de imágenes sintéticas y 29 pares de imágenes multi-foco reales, y el Algoritmo FM-VV sobre 5 tríos de imáge-nes multi-foco. En todos los ejemplos se obtuvo un porcentaje de acierto competitivos, producidos en tiempos de ejecución menores a los reportados en la literatura. Palabras Clave: Fusión de imágenes multi-foco, Ventanas deslizantes, Imágenes integrales Multi Focus Image Fusion with variable size windows AbstractIn this paper we present the Linear Image Combination Algorithm with Variable Windows (CLI-VV) for the fusion of multifocus images. Unlike the CLI-S algorithm presented in a previous work, the CLI-VV algorithm allows to automatically determine the optimal size of the window in each pixel for the segmentation of the regions with the highest sharpness. We also present the generalized CLI-VV Algorithm for the fusion of sets of multi-focus images with more than two images. This new algorithm is called Variable Windows Multi-focus Fusion (FM-VV). The CLI-VV Algorithm was tested with 21 pairs of synthetic images and 29 pairs of real multi-focus images, and the FM-VV Algorithm on 5 trios of multi-focus images. In all the tests a competitive accuracy was obtained, with execution times lower than those reported in the literature. Keywords:Multi-focus image fusion, Sliding windows, Incremental images. IntroducciónHay muchas aplicaciones en las que se involucran imáge-nes, por ejemplo, en sistemas de seguridad, en fotografía, en apoyo al diagnóstico con imágenes médicas, en imágenes satelitales y en algunos ambientes de investigación. En ellas es deseable que las imágenes obtenidas tengan suficiente claridad o nitidez en todos los objetos de la escena. Sin embargo, lo anterior no siempre es posible pues el rango de distancias medidos desde la lente hasta los objetos que se desean capturar no es constante y las cámaras, microscopios y otros objetos que usan lentes para la captura de las imágenes, tienen limitaciones físicas que impiden que todos los objetos de la escena tengan la misma claridad en la imagen digital generada. A dicha limitación se le conoce como profundidad de campo, término que hace referencia al rango de distancias que la lente de una cáma-ra, con una apertura dada, es capaz de reproducir de manera nítida (Kuthirummal et al., 2011).En términos ideales, la luz reflejada por cada punto de la escena debería incidir sobre unúnico punto en el sensor de la cámara, sin em...

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“…Li et al [13] explored guided filtering (GFF) and spatial information to improve the fusion results by mitigating the block effects. Zhang et al [14] proposed a multifocus image scheme based upon a visual saliency method. Recently, image matting has been used for effectively differentiating the focused and out-of-focus regions.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Image Matting Based Multi-Focus Image Fusion Technique

et al. 2020

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Multi-focus image fusion is a very essential method of obtaining an all focus image from multiple source images. The fused image eliminates the out of focus regions, and the resultant image contains sharp and focused regions. A novel multiscale image fusion system based on contrast enhancement, spatial gradient information and multiscale image matting is proposed to extract the focused region information from multiple source images. In the proposed image fusion approach, the multi-focus source images are firstly refined over an image enhancement algorithm so that the intensity distribution is enhanced for superior visualization. The edge detection method based on a spatial gradient is employed for obtaining the edge information from the contrast stretched images. This improved edge information is further utilized by a multiscale window technique to produce local and global activity maps. Furthermore, a trimap and decision maps are obtained based upon the information provided by these near and far focus activity maps. Finally, the fused image is achieved by using an enhanced decision maps and fusion rule. The proposed multiscale image matting (MSIM) makes full use of the spatial consistency and the correlation among source images and, therefore, obtains superior performance at object boundaries compared to region-based methods. The achievement of the proposed method is compared with some of the latest techniques by performing qualitative and quantitative evaluation.

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Multi-focus image fusion algorithm based on focused region extraction

Cited by 43 publications

References 41 publications

Robust sparse representation based multi-focus image fusion with dictionary construction and local spatial consistency

Robust sparse representation based multi-focus image fusion with dictionary construction and local spatial consistency

Fusión de Imágenes Multi-Foco con Ventanas Variables

Multiscale Image Matting Based Multi-Focus Image Fusion Technique

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