IMAGE FUSION FOR MULTIFOCUS IMAGES USING SPEEDUP ROBUST FEATURES

The multi-focus image fusion technique has emerged as major topic in image processing in order to generate all focus images with increased depth of field from multi focus photographs. Image fusion is the process of combining relevant information from two or more images into a single image. The image registration technique includes the entropy theory. Speed up Robust Features (SURF), feature detector and Binary Robust Invariant Scalable Key points (BRISK) feature descriptor is used in feature matching process. An improved RANDOM Sample Consensus (RANSAC) algorithm is adopted to reject incorrect matches. The registered images are fused using stationary wavelet transform (SWT).The experimental results prove that the proposed algorithm achieves better performance for unregistered multiple multi-focus images and it especially robust to scale and rotation translation compared with traditional direct fusion method.  Â

Multi-Focus Image Fusion in DCT Domain using Variance and Energy of Laplacian and Correlation Coefficient for Visual Sensor Networks

The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras. A lot of multi-focus image fusion techniques have been introduced using considering the focus measurement in the spatial domain. However, the multi-focus image fusion processing is very time-saving and appropriate in discrete cosine transform (DCT) domain, especially when JPEG images are used in visual sensor networks (VSN). So the most of the researchers are interested in focus measurements calculation and fusion processes directly in DCT domain. Accordingly, many researchers developed some techniques which are substituting the spatial domain fusion process with DCT domain fusion process. Previous works in DCT domain have some shortcomings in selection of suitable divided blocks according to their criterion for focus measurement. In this paper, calculation of two powerful focus measurements, energy of Laplacian (EOL) and variance of Laplacian (VOL), are proposed directly in DCT domain. In addition, two other new focus measurements which work by measuring correlation coefficient between source blocks and artificial blurred blocks are developed completely in DCT domain. However, a new consistency verification method is introduced as a post-processing, improving the quality of fused image significantly. These proposed methods reduce the drawbacks significantly due to unsuitable block selection. The output images quality of our proposed methods is demonstrated by comparing the results of proposed algorithms with the previous algorithms

Multi Focus Image Fusion with variable size windows

[EN] In 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.[ES] En 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 tamaño óptimo 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 imágenes 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.Calderon, F.; Garnica-Carrillo, A.; Flores, JJ. (2018). Fusión de Imágenes Multi-Foco con Ventanas Variables. 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