Multifocus image fusion using artificial neural networks

Li, Shutao; Kwok, James T.; Wang, Yaonan

doi:10.1016/s0167-8655(02)00029-6

Cited by 260 publications

(108 citation statements)

References 11 publications

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“…Similarly the quad-tree structure method is proposed in [11] to solve the problem of how to determine the size of sub-blocks. Further two more block based approaches are proposed in [12] and [13] to evaluate the local content (sharp) information of the input source images by which the blocking effect in the fused image will reduces efficiently. Though these approaches achieved an efficient fusion performance, the blocking effect is not eliminated completely.…”

Section: Literature Surveymentioning

confidence: 99%

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

Harijana¹,

Gandluru²,

Nanjappa³

2018

IJIES

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Abstract:Medical image fusion is a powerful tool in the medical oriented applications such as diagnosis, treatment planning and image-guided radiotherapy. In this paper, a new medical image fusion approach is proposed based on Non-Subsampled Contourlet Transform (NSCT) and Adaptive sub band filtering. Though there are so many approaches proposed in earlier to find a more and clear fused image, they didn't focus on the computational complexity. Since the computational complexity directly related to the feature count, the rise in the level number of sub band images rises the computational complexity. The adaptive sub band filtering mechanism retrieves the informative sub band images only form all the obtained high frequency sub band images. Further to improve the fusion performance, the proposed approach fuses the low frequency subband images based on phase congruency rule and the high frequency subband images based on Log-Gabor energy rule. Visually and quantitatively experimental results indicate that the proposed fusion method can obtain more effective and accurate fusion results of medical images than other algorithms.

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Section: Literature Surveymentioning

confidence: 99%

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

Harijana¹,

Gandluru²,

Nanjappa³

2018

IJIES

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“…Li et al en (Li et al, 2017) presentan una gráfica que muestra el incremento en la cantidad de artículos relacionados con este tema en losúltimos años. Dentro de la literatura relacionada con el tema encontramos propuestas de solución que van desde aquellas en las que se propone calcular la imagen fusionada como un promedio de las imágenes fuente, hasta técnicas en las que se propone el uso de evolución diferencial (Aslantas and Kurban, 2010) y otros trabajos que utilizan redes neuronales artificiales para el cálculo del mapa de decisión (Li et al, 2002;Zhou et al, 2006;Ma et al, 2011;Pagidimarry and Babu, 2011).…”

Section: Antecedentesunclassified

“…Existen propuestas en las que se utilizan redes neuronales para la fusión de imágenes (Li et al, 2002;Zhou et al, 2006;Ma et al, 2011;Pagidimarry and Babu, 2011). En algunos otros trabajos se propone tratar el problema como un proceso de optimización para el cálculo de los pesos que se asocian a cada píxel de las imágenes Garnica, 2014) y (Calderon et al, 2016).…”

Section: Antecedentesunclassified

“…La segunda técnica propone resolver el problema tomando bloques de píxeles en lugar de tratarlos de forma independiente (Zhang and Blum, 1999;Li et al, 2001Li et al, , 2002Zhao et al, 2008;Aslantas and Kurban, 2010;Pagidimarry and Babu, 2011;De and Chanda, 2013;Qu et al, 2014;Bai et al, 2015;Cao et al, 2015;Calderon et al, 2016).…”

Section: Trabajos Basados En Procesamiento Por Bloques O Por Píxelesunclassified

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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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“…This introduction was resulted in various techniques used for fusion of images from different sensors in order to produce the composite image. This composite image can be further utilized for efficient identification of both natural and man-made objects [1]. Various researchers have started doing experiments in the field of image fusion and their literatures different terms like combination, integration, merging etc were introduced.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-PCA Hybrid Approach For Image Fusion Using Laplacian Features

Kaur¹

2017

IJRASET

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The process of acquiring a clear image by combining two blur images is known as image fusion. In order to accomplish this process, a novel approach has proposed in this paper which collaborates two medical images together to form a fused image. The proposed technique is the hybridization of two techniques which is used to evaluate the weight values using PCA and makes decision about these weight values using fuzzy approach. The designed fuzzy logics let the system to choose the relevant weight values for the manipulations of pixels. Furthermore, the proposed technique is compared with the traditional technique using MATLAB software tool to evaluate the performance. Both the techniques are compared on the basis of three parameters such as AMI, AQI and PSNRusing five different sets of edical images. From the results acquired, it has shown that proposed technique performs effectively better than the traditinal technique in terms of Quality, mutual information and Signal to noise ratio.

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Multifocus image fusion using artificial neural networks

Cited by 260 publications

References 11 publications

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

Fusión de Imágenes Multi-Foco con Ventanas Variables

Fuzzy-PCA Hybrid Approach For Image Fusion Using Laplacian Features

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