Estimating 3D Surface Depth Based on Depth-of-Field Image Fusion

Today’s research era, image fusion is a actual step by step procedure to develop the visualization of any image. It integrates the essential features of more than a couple of images into a individual fused image without taking any artifacts. Multifocus image fusion has a vital key factor in fusion process where it aims to increase the depth of field using extracting focused part from different multiple focused images. In this paper multi-focus image fusion algorithm is proposed where non local mean technique is used in stationary wavelet transform (SWT) to get the sharp and smooth image. Non-local mean function analyses the pixels belonging to the blurring part and improves the image quality. The proposed work is compared with some existing methods. The results are analyzed visually as well as using performance metrics.

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Extended Depth of Field Microscopy for Single Shot 3D Surface Profiling

10.1364/ais.2021.jm4a.1 ◽

2021 ◽

Author(s):

Pol Martínez ◽

Carlos Bermudez ◽

Roger Artigas ◽

Guillem Carles

Keyword(s):

Depth Of Field ◽

Single Shot ◽

Surface Profiling ◽

3D Surface ◽

Extended Depth Of Field

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Extending depth-of-field by image fusion using multi-objective genetic algorithm

2009 7th IEEE International Conference on Industrial Informatics ◽

10.1109/indin.2009.5195826 ◽

2009 ◽

Cited By ~ 2

Author(s):

Veysel Aslantas ◽

Rifat Kurban

Keyword(s):

Genetic Algorithm ◽

Image Fusion ◽

Depth Of Field ◽

Multi Objective ◽

Multi Objective Genetic Algorithm

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A Study on Multi-Focus Images Fusion Technology Based on Fiber Natural Boundaries

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.900.547 ◽

2014 ◽

Vol 900 ◽

pp. 547-553 ◽

Cited By ~ 2

Author(s):

Xin Xu ◽

Rong Wu Wang

Keyword(s):

Image Fusion ◽

Light Microscope ◽

Depth Of Field ◽

Data Loss ◽

Fusion Technology ◽

Fusion Algorithm ◽

Clear Image ◽

The One ◽

Natural Boundaries ◽

Local Gradient

It is difficult to capture a completely clear image of nonwoven web which is thicker than the depth of field of a light microscope. This phenomenon will leads to the data loss and the test error. In this paper, a region-based image fusion algorithm based on fibers natural boundaries was proposed. First, the one-pixel-wide boundaries were extracted from the point-based image fusion process. Then, the image fusion regions were formed by the sharpness diffusion from the source points which have the highest sharpness at local gradient within the boundaries of fibers. Finally, a fused clear image of the nonwoven web was constructed by replacing the regions with the corresponding regions which have the highest sharpness gradient from the series images at different focus positions of the light microscope.

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Construction of All-in-Focus Images Assisted by Depth Sensing

Sensors ◽

10.3390/s19061409 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1409 ◽

Cited By ~ 1

Author(s):

Hang Liu ◽

Hengyu Li ◽

Jun Luo ◽

Shaorong Xie ◽

Yu Sun

Keyword(s):

Image Fusion ◽

Imaging System ◽

State Of The Art ◽

Depth Map ◽

Depth Of Field ◽

Depth Sensor ◽

Depth Sensing ◽

Focus Image ◽

Limited Depth ◽

Image Fusion Method

Multi-focus image fusion is a technique for obtaining an all-in-focus image in which all objects are in focus to extend the limited depth of field (DoF) of an imaging system. Different from traditional RGB-based methods, this paper presents a new multi-focus image fusion method assisted by depth sensing. In this work, a depth sensor is used together with a colour camera to capture images of a scene. A graph-based segmentation algorithm is used to segment the depth map from the depth sensor, and the segmented regions are used to guide a focus algorithm to locate in-focus image blocks from among multi-focus source images to construct the reference all-in-focus image. Five test scenes and six evaluation metrics were used to compare the proposed method and representative state-of-the-art algorithms. Experimental results quantitatively demonstrate that this method outperforms existing methods in both speed and quality (in terms of comprehensive fusion metrics). The generated images can potentially be used as reference all-in-focus images.

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