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

Image Fuzzy Enhancement Based on Self-Adaptive Bee Colony Algorithm

In the image acquisition or transmission, the image may be damaged and distorted due to various reasons; therefore, in order to satisfy people’s visual effects, these images with degrading quality must be processed to meet practical needs. Integrating artificial bee colony algorithm and fuzzy set, this paper introduces fuzzy entropy into the self-adaptive fuzzy enhancement of image so as to realize the self-adaptive parameter selection. In the meanwhile, based on the exponential properties of information increase, it proposes a new definition of fuzzy entropy and uses artificial bee colony algorithm to realize the self-adaptive contrast enhancement under the maximum entropy criterion. The experimental result shows that the method proposed in this paper can increase the dynamic range compression of the image, enhance the visual effects of the image, enhance the image details, have some color fidelity capacity and effectively overcome the deficiencies of traditional image enhancement methods

Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

Fuzzy-Based Histogram Partitioning for Bi-Histogram Equalisation of Low Contrast Images

The conventional histogram equalisation (CHE), though being simple and widely used technique for contrast enhancement, but fails to preserve the mean brightness and natural appearance of images. Most of the improved histogram equalisation (HE) methods give better performance in terms of one or two metrics and sacri ce their performance in terms of other metrics. In this paper, a novel fuzzy based bi-HE method is proposed which equalises low contrast images optimally in terms of all considered metrics. The novelty of the proposed method lies in selection of fuzzy threshold value using level-snip technique which is then used to partition the histogram into segments. The segmented sub-histograms, like other bi-HE methods, are equalised independently and are combined together. Simulation results show that for widerange of test images, the proposed method improves the contrast while preserving other characteristics and provides good trade-off among all the considered performance metrics.This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant DF-374-135-1441

Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction

Photographs taken with less-than-ideal exposure settings often display poor visual quality. Since the correction procedures vary significantly, it is difficult for a single neural network to handle all exposure problems. Moreover, the inherent limitations of convolutions, hinder the models ability to restore faithful color or details on extremely over-/under- exposed regions. To overcome these limitations, we propose a Macro-Micro-Hierarchical transformer, which consists of a macro attention to capture long-range dependencies, a micro attention to extract local features, and a hierarchical structure for coarse-to-fine correction. In specific, the complementary macro-micro attention designs enhance locality while allowing global interactions. The hierarchical structure enables the network to correct exposure errors of different scales layer by layer. Furthermore, we propose a contrast constraint and couple it seamlessly in the loss function, where the corrected image is pulled towards the positive sample and pushed away from the dynamically generated negative samples. Thus the remaining color distortion and loss of detail can be removed. We also extend our method as an image enhancer for low-light face recognition and low-light semantic segmentation. Experiments demonstrate that our approach obtains more attractive results than state-of-the-art methods quantitatively and qualitatively.Comment: Accepted by ACM MM 202

Processing remotely sensed data for geological content over a part of the Barberton Greenstone Belt, Republic of South Africa.

Various methods and techniques developed by researchers worldwide for enhancement and processing ATM, MSS· and TM remotely sensed data are tested. on LANDSAT 5 Thematic Mapper data from a part of the Barberton Greenstone Belt straddling the border between the Republic of South Africa and the Kingdom of Swaziland. Various enhancement techniques employed to facilitate the extraction of structural features and lineaments, and the findings Of the ensuing photogeologlcal interpretation are compared with existing geological maps~ Methods for the detection of zones of hydrothermal alteration. are also considered. The reflectance from vegetation, both natural and cultivated, and the possible reduction of the interference caused by this reflectance, are considered in detail. Partial unmixing of reflectances through the use of various methods and techniques, some of which are readily available from the literature, are performed and its effectiveness tested. Since large areas within the study area are covered by plantations, the interfereiice from the two types of vegetation present (i.e. natural and cultivated), were initially considered separately. In an attempt to isolate the forested areas from the natural vegetation, masks derived through image classification were used to differentially enhance the various features. Results indicate that the use of any particular method to the exclusion of all others will seriously limit the scope of conclusions possible through interpretation of the information present. Enhancement of information in one domain will inadvertently lead to the suppression of information from one or more of the coexisting domains. A series of results from a sequence of procedures interpreted in parallel will in every case produce information of a higher decision making quality.AC201

Global Properties of the Rich Cluster ABCG 209 at z~0.2. Spectroscopic and Photometric Catalogue

This paper is aimed at giving an overview of the global properties of the rich cluster of galaxies ABCG 209. This is achieved by complementing the already available data with new medium resolution spectroscopy and NIR photometry which allow us to i) analyse in detail the cluster dynamics, distinguishing among galaxies belonging to different substructures and deriving their individual velocity distributions, using a total sample of 148 galaxies in the cluster region, of which 134 belonging to the cluster; ii) derive the cluster NIR luminosity function; iii) study the Kormendy relation and the photometric plane of cluster early-type galaxies (ETGs). Finally we provide an extensive photometric (optical and NIR) and spectroscopic dataset for such a complex system to be used in further analyses investigating the nature, formation and evolution of rich clusters of galaxies. The observational scenario confirms that ABCG 209 is presently undergoing strong dynamical evolution with the merging of two or more subclumps. This interpretation is also supported by the detection of a radio halo (Giovannini et al. 2006) suggesting that there is a recent or ongoing merging. Cluster ETGs follow a Kormendy relation whose slope is consistent with previous studies both at optical and NIR wavelengths. We investigate the origin of the intrinsic scatter of the photometric plane due to trends of stellar populations, using line indices as indicators of age, metallicity and alpha/Fe enhancement. We find that the chemical evolution of galaxies could be responsible for the intrinsic dispersion of the Photometric Plane.Comment: 39 pages, 17 figures, MNRAS in pres