The present study aims to evaluate the possibility of simultaneous measurements of Fried parameter and atmospheric Zernike defocus by measuring defocus aberration with 4-aperture differential image motion monitor (DIMM) for a portable telescope. Based on the results, a relation was observed between the variances of the defocus aberration (4-aperture defocus) and Fried parameter for G-tilt and Z-tilt methods. In addition, the variance of the 4-aperture defocus was compared with those of the Zernike defocus and the conventional DIMM and the results indicated a linear relationship. Based on the telescope and 4-aperture specifications, the variance of the 4-aperture defocus was converted into the Zernike defocus or the DIMM variances. Finally, the ability of estimating atmospheric coherence time by measuring the variance of the Zernike defocus velocity or the sum of the variances of two astigmatisms velocities with 4-aperture DIMM was investigated. 相似文献
Due to limited depth-of-field of digital single-lens reflex cameras, the scene content within a limited distance from the imaging plane remains in focus while other objects closer to or further away from the point of focus appear as blurred (out-of-focus) in the image. Multi-Focus Image Fusion can be used to reconstruct a fully focused image from two or more partially focused images of the same scene. In this paper, a new Fuzzy Based Hybrid Focus Measure (FBHFM) for multi-focus image fusion has been proposed. Optimal block size is very critical step for multi-focus image fusion. Particle Swarm Optimization (PSO) algorithm has been used to find optimal size of the block of the images for extraction of focus measure features. After finding optimal blocks, three focus measures Sum of Modified Laplacian, Gray Level Variance and Contrast Visibility has been extracted and combined these focus measures by using intelligent fuzzy technique. Fuzzy based hybrid intelligent focus values were estimated using contrast visibility measure to generate focused image. Different sets of multi-focus images have been used in detailed experimentation and compared the results with state-of-the-art existing techniques such as Genetic Algorithm (GA), Principal Component Analysis (PCA), Laplacian Pyramid discrete wavelet transform (DWT), and aDWT for image fusion. It has been found that proposed method performs well as compare to existing methods.
A novel method is proposed for defocus map estimation. It is based on the defocus origin that is essentially the reverse of depth from defocus (DFD). The main relations among image defocus, sensor defocus, and scene defocus for an imaging system are introduced. A defocus map is deduced from the depth map and the depth map is derived from the disparity map. The full disparity map can be reconstructed using an image-matching method and our clustering segmentation algorithm. Experimental results for an interior scene and an outdoor scene demonstrate that our method is effective in defocus measurement. 相似文献