scholarly journals A neuro fuzzy image fusion using block based feature level method

2020 ◽  
Vol 9 (3) ◽  
pp. 195
Author(s):  
S. Mary Praveena ◽  
R. Kanmani ◽  
A. K. Kavitha
Keyword(s):  
Image Processing ◽  
Image Fusion ◽  
Stereo Matching ◽  
Focal Length ◽  
Depth Of Field ◽  
Fusion Technique ◽  
Neuro Fuzzy ◽  
Focus Image ◽  
Block Based ◽  
Fuzzy Fusion

Image fusion is a sub field of image processing in which more than one images are fused to create an image where all the objects are in focus. The process of image fusion is performed for multi-sensor and multi-focus images of the same scene. Multi-sensor images of the same scene are captured by different sensors whereas multi-focus images are captured by the same sensor. In multi-focus images, the objects in the scene which are closer to the camera are in focus and the farther objects get blurred. Contrary to it, when the farther objects are focused then closer objects get blurred in the image. To achieve an image where all the objects are in focus, the process of images fusion is performed either in spatial domain or in transformed domain. In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Thus it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. Hence, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images. Thus the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique is presented.  The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices.

scholarly journals Multi-focus Image Fusion Based on Decision Map and Sparse Representation

2019 ◽  
Vol 9 (17) ◽  
pp. 3612
Author(s):  
Liao ◽  
Chen ◽  
Mo
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Focal Length ◽  
The Other ◽  
Fusion Method ◽  
Optical Lens ◽  
Transitional Area ◽  
Fusion Methods ◽  
Focus Image ◽  
Image Fusion Method

As the focal length of an optical lens in a conventional camera is limited, it is usually arduous to obtain an image in which each object is focused. This problem can be solved by multi-focus image fusion. In this paper, we propose an entirely new multi-focus image fusion method based on decision map and sparse representation (DMSR). First, we obtained a decision map by analyzing low-scale images with sparse representation, measuring the effective clarity level, and using spatial frequency methods to process uncertain areas. Subsequently, the transitional area around the focus boundary was determined by the decision map, and we implemented the transitional area fusion based on sparse representation. The experimental results show that the proposed method is superior to the other five fusion methods, both in terms of visual effect and quantitative evaluation.

Efficient Multi Focus Image Fusion Technique Optimized Using MOPSO for Surveillance Applications

2018 ◽  
Vol 14 (3) ◽  
pp. 18-37 ◽  
Author(s):  
Nirmala Paramanandham ◽  
Kishore Rajendiran
Keyword(s):  
Spatial Frequency ◽  
Image Fusion ◽  
Objective Assessment ◽  
Similarity Index ◽  
Absolute Error ◽  
Activity Level ◽  
Fusion Technique ◽  
Focus Image ◽  
Image Fusion Technique ◽  
Surveillance Applications

This article describes how image fusion has taken giant leaps and emerged as a promising field with diverse applications. A fused image provides more information than any of the source images and it is very helpful in surveillance applications. In this article, an efficient multi focus image fusion technique is proposed in cascaded wavelet transform domain using swarm intelligence and spatial frequency (SF). Spatial frequency is used for computing the activity level and consistency verification (CV) based decision map is employed for acquiring the final fused coefficients. Justification for employing SF and CV is also discussed. This technique performs well compared to existing techniques even when the source images are severely blurred. The proposed framework is evaluated using quantitative metrics, such as root mean square error, peak signal to noise ratio, mean absolute error, percentage fit error, structural similarity index, standard deviation, mean gradient, Petrovic metric, SF, feature mutual information and entropy. Experimental outcomes demonstrate that the proposed technique outperforms the state-of-the art techniques, in terms of visual impact as well as objective assessment.

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

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1409 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document