Multi Focus Research Articles

Efficient Multi Focus Image Fusion Technique Optimized Using MOPSO for 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.

A Review on Various Techniques of Image Fusion for Quality Improvement of Images

Image fusion is a significant topic in perspective processing. Image fusion is a process of mixing the appropriate data from some images into a single image where the resulting merged picture will be more useful and complete than any of the input images. Multi focus Image fusion is procedure of combining information of several imagery of a view and consequently has \everywhere in focus "image. Lifting technique allows faster implementation of wavelet transform. It requires half number of computations as compared to traditional convolution approach.

A FUZZY EDGE BASED MULTIFOCUS FUSION

A New method of fusion is presented to find each object present in the image. The proposed algorithm used USAN (univalue segment assimilating nucleus) technique to fuse the high and low intensity edges. The effective way of multi focus fusion is based on the fuzzy edge detection using modified fuzzy membership function. However, the proposed method computes the edge intensity region in an optimal way by using particle swarm optimization (PSO). We conduct experiments on several images and compare with different state of art methods. The effectiveness of the proposed approach outperforms than other methods in each case of performance measures.

Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

Poor illumination, less background contrast and blurring effects makes the medical, satellite and camera images difficult to visualize. Image fusion plays the vital role to enhance image quality by resolving the above issues and reducing the image quantity. The combination of spatial and spectral technique Discrete Wavelet Transform and Principal Component Analysis (DWT-PCA) decrease processing time and reduce number of dimensions but down sampling causes lack of shift invariance that results in poor quality final fused image. At first this work uses combined median and average filter that eliminates noise in the image which is caused by illumination, camera circuitry and sensor at preprocessing stage. Then, hybrid Stationary Wavelet Transform and Principal Component Analysis (SWT-PCA) technique is implemented to increase output image accuracy by eliminating down sampling and is not influenced by artifacts and blurring effects. Further, it can overcome the trade-off of Heisenberg’s uncertainty principle by improving accuracy in both domains, time (spatial) as well as frequency (spectral). The proposed combined median and average filter with hybrid SWT-PCA algorithm measures quality parameters, such as peak signal to noise ratio (PSNR), mean squared error (MSE) and normalized cross correlation (NCC) and improved results depict the superiority of the algorithm than existing techniques.

Temporal focus clusters differ meaningfully in terms of anxiety and depressive symptomatology

The present study examined the relationship between temporal focus profiles and scores on psychiatric symptomatology. Participants were recruited by undergraduate students in a university in the United Kingdom (N = 372; 46% male). Five clusters emerged from hierarchical cluster analysis and were labelled, No Focus, Multi Focus, Past Focus, Future Focus, and Current Focus. The worst outcomes in terms of psychiatric symptomatology were observed for those belonging to the Past Focus cluster. These results build on the only previous study using cluster analysis of Temporal Focus Scale scores, where being past focussed was associated with lower self-esteem in adolescents.

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.

Image Fusion using Variational Mode Decomposition

Background/Objectives: This paper introduced an image fusion algorithm based on Variational Mode Decomposition (VMD). Methods/Statistical Analysis: Image fusion is one of the image enhancement methods which results the image with better quality derived from a set of degraded images. Fused image contains more information than input images and it is efficient for visual perception and computer vision applications. This paper proposed an image fusion technique based on VMD for multi focus images. VMD has been a recently introduced non-recursive decomposition method, which decomposes the image into separate spectral bands called Intrinsic Mode Function (IMF) or modes. The modes are generated with respect to the associated central frequencies and they are band limited. Findings: A fusion rule based on weighing scheme is performed at the decomposition level for increasing the features by decreasing the mutual information. The reconstruction of the IMFs results the final fused image. The performance analysis of the proposed fusion method is experimented using standard objective quality metrics. The efficiency of the proposed method is determined by comparing the method with some state of the art methods. Application/Improvements: The image fusion using VMD is applicable to multi-resolution, multi model multi-sensor images.

Image Fusion on Coloured and Gray Scale Multi Focus Images by using Hybrid DWT-DCT

Any piece of information is meaningful only when it is able to convey the content about it. The clarity and quality of information is important. Image Fusion is a technique to improve the quality and quantity of information from a set of images. By the process of image fusion the more information from each of the given images is combining together to generate a resultant image whose quality is maximum to any of the input images. We proposed a Hybrid DWT-DCT method to fuse multi focus images. In this technique we convert the image data from spatial domain to transform domain.. Then decompose the transform data into four parts that is LL, LH, HL, HH part. This decomposition Process again applies in LL part at two levels. After this three level decomposition we combine the input image data by applying average method using DCT and get fused data. This data are in transform domain again convert to spatial domain by applying IDCT and IDWT method and get final fused image with better visual Quality. After getting the result of fused image we compare the quality measure parameters of different technique like PCA, DCT, average pixel, maximum pixel, minimum pixel, HDWT method to hybrid DWT-DCT method. And conclude that the PSNR value and Entropy of fused image have better result as compare to other techniques. Due to this the fused image has better visual Quality as well as more informative data would contain in fused image.

An Image Fusion Technique Based on Hadamard Transform and HVS

The main endeavor of image fusion is to obtain an image that contains more visual quality information than any one of the source images. In general, the source images may be multi focus, multi modality, multi resolution, multi temporal, panchromatic, satellite images considered for fusion. This paper discusses image fusion using Hadamard Transform (HT). In this work, Human Visual System (HVS) is investigated for image fusion in the HT domain. The proposed fusion process contains three important parts, (1) divide source images into sub images / blocks and transform them into HT domain. (2) multiply transformed coefficients with HVS based weightage matrix of HT and select the highest value from them (3) fuse the corresponding block of selected coefficients from source images in to an empty image. The utility of HVS makes the coefficients more significant. The performance of the proposed method is analyzed and compared with Discrete Wavelet Transform (DWT) based image fusion technique. Implementation in HT domain is simple and time saving when compared with DWT.

Which One to Choose Multi Focus or Trade-Off Among Competitive Priorities? Evidence from Finnish SMEs

AbstractThis paper examines the relationship between business environment, competitiveness and firm performance considering a survey data from three consecutive years: 2013-2015. The comparative analysis showed that over these years business environment, competitiveness and firm performance of Finnish SMEs are slowly deteriorating. Results from correlation analysis revealed that business environment, competitiveness and firm performance are positively correlated. However, the relationships between these variables are not consistent in respect to competitive priorities indicating a dynamic nature of cost, quality, time, and flexibility dimensions. Besides these findings our analysis acknowledged that to improve firm performance, irrespective to the choice of competitive priority, SMEs should pay more attention to their competitiveness rather than blaming the business environment. The paper concludes that simultaneous use of competitive priority dimensions might be more favorable as a source of competitiveness and competitive advantage to improve firm performance. However, the managers are encouraged to compare the results, findings and concepts presented in this paper among themselves and comprehend the specific answer to the question posed in the title.

All in focus fusion using guided filter

A guided filter based fusion scheme for multi focus images is proposed. The source images are decomposed into base and detail layers. The base layers contain the large scale variations and are averaged out to obtain the base layer of the fused image. The weights of detail layers are computed based on whether the objects in a particular image is in focus compared to the same object in all other images. Guided filtering is performed to further refine the weights. Simulation results reveal the significance of proposed scheme.

A pixel based multi-focus image fusion method

The lenses used in computer vision systems by nature provide limited depth of field, so digital cameras are incapable of acquiring an all-in-focus image of objects at varying distances in a scene. To obtain an all-in-focus image of such scenes from their differently focused images, this paper proposes a novel spatial domain multi focus image fusion technique. The developed technique, firstly, computes the point spread functions (PSF) of the source images. Next, the images are artificially blurred by convolving them with the estimated PSFs. Then, the artificially blurred images are used to determine the sharpest pixels of the source images. Finally, the all-in-focus image of the scene is constructed by gathering the sharpest pixels of the source images. Experiments are carried out on several multi-focus image sets. The proposed method and other well-known image fusion methods are compared in terms of visual and quantitative evaluation. The results obtained show the feasibility of the developed technique.

Multifocus Images Fusion Based On Homogenity and Edges Measures

Image fusion is one of the most important techniques in digital image processing, includes the development of software to make the integration of multiple sets of data for the same location; It is one of the new fields adopted in solve the problems of the digital image, and produce high-quality images contains on more information for the purposes of interpretation, classification, segmentation and compression, etc. In this research, there is a solution of problems faced by different digital images such as multi focus images through a simulation process using the camera to the work of the fuse of various digital images based on previously adopted fusion techniques such as arithmetic techniques (BT, CNT and MLT), statistical techniques (LMM, RVS and WT) and spatial techniques (HPFA, HFA and HFM). As these techniques have been developed and build programs using the language MATLAB (b 2010). In this work homogeneity criteria have been suggested for evaluation fused digital image's quality, especially fine details. This criterion is correlation criteria to guess homogeneity in different regions within the image by taking a number of blocks of different regions in the image and different sizes and work shifted blocks per pixel. As dependence was on traditional statistical criteria such as (mean, standard deviation, and signal to noise ratio, mutual information and spatial frequency) and compared with the suggested criteria to the work. The results showed that the evaluation process was effective and well because it took into measure the quality of the homogenous regions.

Multifocus Images Fusion Based On Homogenity and Edges Measures

Image fusion is one of the most important techniques in digital image processing, includes the development of software to make the integration of multiple sets of data for the same location; It is one of the new fields adopted in solve the problems of the digital image, and produce high-quality images contains on more information for the purposes of interpretation, classification, segmentation and compression, etc. In this research, there is a solution of problems faced by different digital images such as multi focus images through a simulation process using the camera to the work of the fuse of various digital images based on previously adopted fusion techniques such as arithmetic techniques (BT, CNT and MLT), statistical techniques (LMM, RVS and WT) and spatial techniques (HPFA, HFA and HFM). As these techniques have been developed and build programs using the language MATLAB (b 2010). In this work homogeneity criteria have been suggested for evaluation fused digital image's quality, especially fine details. This criterion is correlation criteria to guess homogeneity in different regions within the image by taking a number of blocks of different regions in the image and different sizes and work shifted blocks per pixel. As dependence was on traditional statistical criteria such as (mean, standard deviation, and signal to noise ratio, mutual information and spatial frequency) and compared with the suggested criteria to the work. The results showed that the evaluation process was effective and well because it took into measure the quality of the homogenous regions.

Effect of complex phase plate on tight focusing of azimuthally polarized double ring shaped beam

Based on the vector diffraction theory, the effect of complex phase plate on the intensity distribution of TEM11 mode azimuthally polarized Laguerre–Gaussian beam in the focal region of high NA lens is investigated theoretically. It is observed that a properly designed complex phase plate can generate a subwavelength focal hole having FWHM of 0.332λ with an extended focal depth of 54.4λ. We also observe that by using properly designed complex phase plate generates novel focal patterns including splitting of focal holes and multi focus are obtained. The author expects such investigation is worthwhile for optical manipulation and material processing technologies.

A taxonomy of manufacturing strategies in manufacturing companies in Ireland

Purpose – The purpose of this paper is to replicate the taxonomic study of Miller and Roth and to validate its applicability in a small newly industrialised country. Design/methodology/approach – An empirical survey of manufacturing companies in Ireland was conducted using a research instrument that replicated the questions asked in the original 1994 study. Data was collected using mail questionnaires posted to 874 manufacturers. A total of 199 respondents were used for this study. The study tested two central hypotheses using advanced statistical data analysis techniques, such as canonical discriminant analysis. Findings – The results of the research identified three clustered strategic groups (Best Value, Budget and Multi Focus) which were different from those of Miller and Roth: caretakers, marketeers and innovators. The study supported the dynamic nature of manufacturing strategy, and shows how new manufacturing strategies evolve over time and differ between regions. Research limitations/implications – Further research should replicate this study in other small newly industrialised countries. Given the recently changed nature of the economy within Ireland, a longitudinal study would provide useful insights into the dynamics of manufacturing capabilities. Practical implications – The study provides important insights into manufacturing strategy within a small newly industrialised country. The results suggest that manufacturing strategies appear to change depending on the country of plant location. The manufacturing strategies may be attributed to the unique business environment, challenges and constraints of the country. Originality/value – There is a scarcity of taxonomy studies in general, and there is a dearth of such studies in small newly industrialised countries such as Ireland. Through identifying a new taxonomy the study contributes to the existing literature on manufacturing strategy, and challenges the global applicability of taxonomies developed in the USA.

Image fusion approach with noise reduction using Genetic algorithm

Image fusion is becoming a challenging field as for its importance to different applications, Multi focus image fusion is a type of image fusion that is used in medical fields, surveillances, and military issues to get the image all in focus from multi images every one is in focus in a different part, and for making the input images more accurate before making the fusing process we use Genetic Algorithm (GA) for image de-noising as a preprocessing process. In our research paper we introduce a new approach that begin with image de-noising using GA and then apply the curvelet transform for image decomposition to get a multi focus image fusion image that is focused in all of its parts. The results show that Curvelet transform had been proven to be effective at detecting image activity along curves, and increasing the quality of the obtained fused images. And applying the mean fusion rule for fusing multi-focus images gives accurate results than PCA, contrast and mode fusion rule, Also, GA shows more accurate results in image de-noising after comparing it to contourlet transform.

Image Fusion Based On Wavelet And Curvelet Transform

In this paper we have put forward an image fusion algorithm based on wavelet transform and second generation curvelet transform. The wavelet transform does not represent the edges and singularities well. So the second generation curvelet transform is performed along with the wavelet transform and the image fusion is done. Finally, the proposed algorithm is applied to experiments of multi focus image fusion and complementary image fusion. The proposed algorithm holds useful information from source multiple images quite well.

A Novel Statistical Fusion Rule for Image Fusion and its Comparison in Non Subsampled Contourlet Transform Domain and Wavelet Domain

Image fusion produces a single fused image from a set of input images. A new method for image fusion is proposed based on Weighted Average Merging Method (WAMM) in the Non Subsampled Contourlet Transform (NSCT) domain. A performance analysis on various statistical fusion rules are also analysed both in NSCT and Wavelet domain. Analysis has been made on medical images, remote sensing images and multi focus images. Experimental results shows that the proposed method, WAMM obtained better results in NSCT domain than the wavelet domain as it preserves more edges and keeps the visual quality intact in the fused image.

Optical distance control for a multi focus image in camera phone module assembly

This study aims at developing a focusing method for an image with multiple focal distances. A dynamic focus region (DFR) is defined to find the object of interest to be focused on. The DFR is built from the edge fragments detected using several narrow ROIs across the vision screen. The proposed method was applied to the camera module assembly. The inspected images usually have multiple areas with different focal distances, due to the different thicknesses of the components. An assembly machine was constructed to verify the DFR for the experiment. The distance between the lens mount and vision camera was adjusted to find the focus position by means of the sharpness and DFR. The result shows that the desired object is brought into focus and the proposed method can be applied to the camera module assembly process.