A survey on applications and variants of the cuckoo search algorithm

A multi-exposure and multi-focus image fusion algorithm is proposed. The algorithm is developed for color images and is based on blending the gradients of the luminance components of the input images using the maximum gradient magnitude at each pixel location and then obtaining the fused luminance using a Haar wavelet-based image reconstruction technique. This image reconstruction algorithm is of O(N) complexity and includes a Poisson solver at each resolution to eliminate artifacts that may appear due to the nonconservative nature of the resulting gradient. The fused chrominance, on the other hand, is obtained as a weighted mean of the chrominance channels. The particular case of grayscale images is treated as luminance fusion. Experimental results and comparison with other fusion techniques indicate that the proposed algorithm is fast and produces similar or better results than existing techniques for both multi-exposure as well as multi-focus images.

Multi-Exposure and Multi-Focus Image Fusion in Gradient Domain

In the field of image processing, there are several problems where the efficient search has to be performed in complex search domain to find an optimal solution Image enhancement which improves the quality of an image for visual analysis and/or machine understanding is one of these problems There is no unique image enhancement technique and it’s measurement criterion which satisfies all the necessity and quantitatively judge the quality of a given image respectively Thus sometimes proper image enhancement problem becomes hard and takes large computational time In order to overcome that problem, researchers formulated the image enhancement as optimization problems and solved using Nature-Inspired Optimization Algorithms (NIOAs) which starts a new era in image enhancement field This study presents an up-to-date review over the application of NIOAs in image enhancement domain The key issues which are involved in the formulation of NIOAs based image enhancement models are also discussed here

A Survey on Nature-Inspired Optimization Algorithms and Their Application in Image Enhancement Domain

In order to fuse two registered multi-spectral (MS) image and panchromatic (PAN) image in the same scene, a new remote sensing image fusion algorithm based on Principal Component Analysis (PCA) and Curvelet transform is proposed. The first principle component PC1 of MS image is extracted via PCA transform, at the same time, we perform the Morphology-Hat transform on the PAN image, and segment the transformed PAN image by the PCNN segmentation algorithm. Perform the Curvelet transform on the component PC1 of MS image and the PAN image after Morphology-Hat transform, and use different fusion rule to fuse different scale layers coefficients (coarse, detail and fine scale layer). For obtaining the fused image, we use the inverse Curvelet transform and inverse PCA transform to obtain the fused image. The experimental results illustrate that the proposed fusion algorithm outperforms Curvelet transform and other traditional fusion algorithms in whole such as intensity–hue–saturation, PCA, Brovey and Weighted Average both in visual effect and objective evaluation indexes (standard deviation, mean, information entropy, correlation coefficient, spectral distortions and deviation index).

Remote Sensing Image Fusion Method Based on PCA and Curvelet Transform

Automated defect inspection system for CMOS image sensor with micro multi-layer non-spherical lens module

Microscopic Image Contrast and Brightness Enhancement Using Multi-scale Retinex and Cuckoo Search Algorithm☆

In this paper, we consider the problem of covering all regions of interests (targets) by relocating a set of mobile sensors such that total movement made by them is minimized. This problem itself is a challenging one and addressed recently by some researchers under free mobility model. We consider a more restricted version of the problem where sensors can move only in two mutually perpendicular directions. We first show that the optimal point to which a sensor must move to cover a specific target is different under this model from the one where sensors can move freely, and characterize such a point. On the basis of this observation, we have developed heuristics to solve the problem. The heuristics run in two phases; the first phase ensures coverage and the second phase, connectivity. In both the phases, the sensors can move only with restricted mobility. We have run a set of experiments to evaluate the performance of the proposed algorithm and found that the total movement made in the first phase is comparable to the solution given by an IPP ( Integer Programming Problem ). For the second phase, we have presented two heuristics MinCon and MinCon_m. The algorithm MinCon works by finding connected components of the graph consisting of sensor nodes. It then identifies destination locations where some sensors must be placed so that all necessary components become connected. Once the destinations are known, the problem is solved by mapping it to an LSAP (Linear Sum Assignment Problem). The other heuristic MinCon_m improves over MinCon by moving only a subset of sensors to their destinations using the solution of LSAP. It then finds the movement of the remaining sensors applying a technique used in the first phase.

Coverage of Targets in Mobile Sensor Networks With Restricted Mobility

Remote Sensing Image Fusion using PCNN Model Parameter Estimation by Gamma Distribution in Shearlet Domain

In this paper a non-sub-sampled shearlet transform model for image enhancement to improve image quality by maximizing the information content in the input image is proposed and implemented. This is inspired by two basic human visual features: i) visual adaptation, ii) local contrast enhancement of color image. We achieve the goal in different stages: In initial stage evolved upon the shearlet transformation of an image from image space to a low-pass and high-pass images in shearlet space and then to the visual response space. The visual adaptation is generally implemented with the Naka-Rushton equation, a rational transformation which allows to accurately compress the range of the input image. The later stage consists in a variational method able to perform local contrast enhancement with Weber-Fechner's law. After modifying the response values, the transformation can be reversed to produce the resulting image. The proposed method Naka-Rushton shearlet Image Enhancement (NRSIE) has been implemented on several color images. The results are compared with two other popular color image enhancement techniques along with visual analysis, detail and background variance. It has been found that NRSIE scales better results compared to two other popular methods.

Automatic contrast enhancement of low-light images based on Naka-Rushton visual adaptation in non-sub-sampled shearlet domain

Wireless sensor networks generally have unique lifetime necessities. In any case, the density of the sensors may not be sufficiently substantial to fulfil the coverage requirement while meeting the lifetime constraint in the mean time. Once in a while coverage has to be traded for network lifetime. The proposed efficient pipeline based spatial temporal optimization scheduling for coverage optimization satisfies the coverage problem while meeting the lifetime constraint at the same time. In the proposed optimal scheduling, initially number of nodes in the network is clustered by using energy based one hop clustering algorithm. After the formation of clusters pipeline based spatial temporal optimization algorithm is used for the optimal scheduling. Here the optimization is improved by using trust of each sensor nodes and the area of clusters. Finally, data is aggregated through the optimally scheduled cluster nodes. The experimental results show that our proposed optimization scheduling substantially outperforms other schemes in terms of network lifetime, coverage redundancy and convergence time.

Ritamshirsa Choudhuri

Papers

Microscopic Image Contrast and Brightness Enhancement Using Multi-scale Retinex and Cuckoo Search Algorithm☆

Coverage of Targets in Mobile Sensor Networks With Restricted Mobility

Remote Sensing Image Fusion using PCNN Model Parameter Estimation by Gamma Distribution in Shearlet Domain

Automatic contrast enhancement of low-light images based on Naka-Rushton visual adaptation in non-sub-sampled shearlet domain

Efficient Area Coverage in Wireless Sensor Networks Using Optimal Scheduling