Optimized Histogram Equalization for Image Enhancement

In this project, Image Enhancement has been achieved by performing Histogram Equalization that uses optimization algorithms to optimize parameters.Histogram equalization is a spatial domain image enhancement technique, which effectively enhances the contrast of an image.However, while it takes care of contrast enhancement,it does not consider the abrupt changes in the image brightness due to which image brightness is not preserved.Hence,in this project a modified histogram equalization technique using optimization algorithm has been proposed, which takes care of contrast enhancement while ensuring brightness preservation.The idea used here is to first ,section the data image histogram into two, utilizing otsu's limit .Then an arrangement of streamlined measuring requirements are formed and connected on both the sub-images. Then, the sub-images are evened out freely and their union creates the contrast enhanced , brightness preserved output image .Here we have used three Optimization Algorithms for finding the optimal constraints . First , Genetic Algorithm(GA) has been used , to optimise the constraints .Second , Particle Swarm Optimization (PSO) has been used and third ,a Hybrid PSO Optimization Algorithm has been used for the same .Then the results produced by the above algorithms are compared to find out which one outperforms the other , by comparing various parameters like Discrete Entropy , Mean , Number of Generations

Infrared image enhancement using adaptive histogram partition and brightness correction

Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods

An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization

Image contrast is an essential visual feature that determines whether an image is of good quality. In computed tomography (CT), captured images tend to be low contrast, which is a prevalent artifact that reduces the image quality and hampers the process of extracting its useful information. A common tactic to process such artifact is by using histogram-based techniques. However, although these techniques may improve the contrast for different grayscale imaging applications, the results are mostly unacceptable for CT images due to the presentation of various faults, noise amplification, excess brightness, and imperfect contrast. Therefore, an ameliorated version of the contrast-limited adaptive histogram equalization (CLAHE) is introduced in this article to provide a good brightness with decent contrast for CT images. The novel modification to the aforesaid technique is done by adding an initial phase of a normalized gamma correction function that helps in adjusting the gamma of the processed image to avoid the common errors of the basic CLAHE of the excess brightness and imperfect contrast it produces. The newly developed technique is tested with synthetic and real-degraded low-contrast CT images, in which it highly contributed in producing better quality results. Moreover, a low intricacy technique for contrast enhancement is proposed, and its performance is also exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). Finally, the proposed technique provided acceptable results with no visible artifacts and outperformed all the comparable techniques

Sequentially Modified Gravitational Search Algorithm for Image Enhancement

Gravitational Search Algorithm (GSA) is based on the acceleration trend feature of objects with a mass towards each other and includes many interdependent parameters. The gravitational constant among these parameters influences the speeds and positions of the agents, meaning that the search capability depends on the largescale gravitational constant. The proposed new algorithm, which was obtained with the use of two operators at different times of the call and sequentially doing works, was named as Sequentially Modified ‎ Gravitational Search Algorithm (SMGSA). SMGSA is applied to 10 basic and 6 composite benchmark functions. Each function is run 30 times and the best, mean and median values are obtained. The achieved results are compared with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSA among the heuristic optimization algorithms. Between GSA and the operator for each function convergence speed, standard deviation and graphical comparisons are included. Beside this, by using the Wilcoxon signed rank test, the comparison of the averages of the data as two dependent groups of GSA and the new operators is performed. It is seen that the obtained results provided better results than the other methods. Additionally, in this study, SMGSA was applied to the transformation function among image enhancement techniques which are engineering applications. The success of this method has been increased by optimizing the parameters of the transformation function used. Effective improvement has been achieved in terms of both visual and information quality

Car make and model recognition under limited lighting conditions at night

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyCar make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when licence plate numbers cannot be identified or fake number plates are used. CMMR can also be used when automatic identification of a certain model of a vehicle by camera is required. The majority of existing CMMR methods are designed to be used only in daytime when most car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. This work identifies car make and model at night by using available rear view features. A binary classifier ensemble is presented, designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and licence plates from the rear view are extracted and used in the recognition process. The majority vote of individual classifiers, support vector machine, decision tree, and k-nearest neighbours is applied to verify a target model in the classification process. The experiments on 100 car makes and models captured under limited lighting conditions at night against about 400 other car models show average high classification accuracy about 93%. The classification accuracy of the presented technique, 93%, is a bit lower than the daytime technique, as reported at 98 % tested on 21 CMMs (Zhang, 2013). However, with the limitation of car appearances at night, the classification accuracy of the car appearances gained from the technique used in this study is satisfied

Image Enhancement for Scanned Historical Documents in the Presence of Multiple Degradations

Historical documents are treasured sources of information but typically suffer from problems with quality and degradation. Scanned images of historical documents suffer from difficulties due to paper quality and poor image capture, producing images with low contrast, smeared ink, bleed-through and uneven illumination. This PhD thesis proposes a novel adaptative histogram matching method to remove these artefacts from scanned images of historical documents. The adaptive histogram matching is modelled to create an ideal histogram by dividing the histogram using its Otsu level and applying Gaussian distributions to each segment with iterative output refinement applied to individual images. The pre-processing techniques of contrast stretching, wiener filtering, and bilateral filtering are used before the proposed adaptive histogram matching approach to maximise the dynamic range and reduce noise. The goal is to better represent document images and improve readability and the source images for Optical Character Recognition (OCR). Unlike other enhancement methods designed for single artefacts, the proposed method enhances multiple (low-contrast, smeared-ink, bleed-through and uneven illumination). In addition to developing an algorithm for historical document enhancement, the research also contributes a new dataset of scanned historical newspapers (an annotated subset of the Europeana Newspaper - ENP – dataset) where the enhancement technique is tested, which can also be used for further research. Experimental results show that the proposed method significantly reduces background noise and improves image quality on multiple artefacts compared to other enhancement methods. Several performance criteria are utilised to evaluate the proposed method’s efficiency. These include Signal to Noise Ratio (SNR), Mean opinion score (MOS), and visual document image quality assessment (VDIQA) metric called Visual Document Image Quality Assessment Metric (VDQAM). Additional assessment criteria to measure post-processing binarization quality are also discussed with enhanced results based on the Peak signal-to-noise ratio (PSNR), negative rate metric (NRM) and F-measure.Keywords: Image Enhancement, Historical Documents, OCR, Digitisation, Adaptive histogram matchin

Signals and Images in Sea Technologies

Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development. It is not difficult to argue that signals and image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Besides increasing the general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructure projects, to the discovery, documentation, and protection of sunken cultural heritage. The scope of this Special Issue encompasses investigations into techniques and ICT approaches and, in particular, the study and application of signal- and image-based methods and, in turn, exploration of the advantages of their application in the previously mentioned areas