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Exploration of the deep sea and ocean in the marine industry has continued to gain interest in recent years. To get the detailed imaging of deep sea layers, marine vessels and robots are fitted with advanced imaging technologies. There are certain factors like water properties and impurities that affect the quality of the photographs captured by the underwater imaging devices. As sea water absorbs colors, so processing of sea imaging data becomes more challenging. Water light attenuation is a phenomenon that is caused by the absorbance and scattering factors. Certain studies showed that the existence of certain intrinsic shortcomings are attributed to the appearance of objects and ambient noise in underwater images. As a result, it is difficult in a real-time system to distinguish objects from their surroundings in these images. We measures the algorithms performance with respect to various aspects, effect of the hardware and software parts for underwater images and critical review of different underwater image enhancement algorithms. First, we describe some well-known techniques of spatial and frequency domains. Then, we list the existing quantitative measurements which are required to measure the quality of the enhanced image. Finally, the performance of various up-to-date existing methods is compared based on the outcomes of standard quantitative measurements, and factors such as requirements/suitability, and technical aspects, are included. Furthermore, a variety of image databases used for image contrast enhancement is discussed in detail. This study expands the scope for other researchers to understand the important characteristics of different underwater image contrast enhancement methods, and also provides future research directions.

PurposeTo evaluate the image quality and diagnostic performance for pancreatic lesion between true non-contrast (TNC) and virtual non-contrast (VNC) images obtained from the dual-energy computed tomography (DECT).MethodsOne hundred six patients with pancreatic mass underwent contrast-enhanced DECT examinations were retrospectively included in this study. VNC images of the abdomen were generated from late arterial (aVNC) and portal (pVNC) phases. For quantitative analysis, the attenuation differences and reproducibility of abdominal organs were compared between TNC and aVNC/pVNC measurements. Qualitatively image quality was assessed by two radiologists using a five-point scale, and they independently compared the detection accuracy of pancreatic lesions between TNC and aVNC/pVNC images. The volume CT dose index (CTDIvol) and size-specific dose estimates (SSDE) were recorded to evaluate the potential dose reduction when using VNC reconstruction to replace the unenhanced phase.ResultsA total of 78.38% (765/976) of the attenuation measurement pairs were reproducible between TNC and aVNC images, and 71.0% (693/976) between TNC and pVNC images. In triphasic examinations, a total of 108 pancreatic lesions were found in 106 patients, and no significant difference in detection accuracy was found between TNC and VNC images (p = 0.587–0.957). Qualitatively, image quality was rated diagnostic (score ≥ 3) in all the VNC images. Calculated CTDIvol and SSDE reduction of about 34% could be achieved by omitting the non-contrast phase.ConclusionVNC images of DECT provide diagnostic image quality and accurate pancreatic lesions detection, which are a promising alternative to unenhanced phase with a substantial reduction of radiation exposure in clinical routine.

Satellite images often have very narrow brightness value ranges, so it is necessary to enhance the contrast and brightness, maintain the quality of visual information, and preserve pertinent details in the images before conducting additional analysis. This is because improving the brightness and contrast of images is crucial to image processing and analysis as it makes it easier for people to identify and comprehend the images. The Incomplete Beta Function (IBF) is a popular transformation function for Image Contrast Enhancement (ICE). Nevertheless, IBF has modest efficiency in parameter selection, a small set of adjustable parameters for stretching regions with high or low gray levels, and image enhancement is almost ineffective with stretching at either end. Meta-heuristic algorithms have been utilized efficiently and effectively over the past few decades to solve complicated image processing problems. This paper presents an Augmented version of the Elk Herd Optimizer (AEHO) combined with other traditional ICE techniques to improve edge details, entropy, local contrast, and local brightness of low-contrast natural and satellite images. The AEHO method employs a multi-stage strategic procedure, where its mathematical model undergoes several enhancements before being applied to ICE to allow for further exploration and exploitation of its features. This method uses a pre-established fitness criterion for the purpose of optimizing a set of parameters to rework a well-known transformation function and an effective assessment technique as an objective standard for this purpose. In the proposed image enhancement model, contrast limited adaptive histogram equalization was first applied as a prior step to ameliorate the color intensity. Then, the optimal IBF's parameters for ICE were adaptively determined using AEHO. After that, bilateral gamma correction was used to improve the visual quality of images without sacrificing edge details or natural color quality. The proposed AEHO-based image enhancement model is tested on natural scenes, certain standard images, and publicly available satellite images. In addition to other five techniques built on based on pre-existing meta-heuristics, the performance of the proposed method was compared against other well-known state-of-the-art image enhancement algorithms. The objective evaluation of the enhancement algorithms was achieved utilizing a variety of full-reference, no-reference, and pertinent performance evaluation norms. The experimental findings illustrated that the proposed image enhancement method can successfully outperform several other algorithms that employed the same image enhancement model as AEHO in addition to other conventional image enhancement methods included for comparison. The results on ten natural and satellite color images showed that the presented method performs better than all other comparative methods in the corresponding evaluation criteria in terms of average peak signal-to-noise ratio, average universal quality index, average structural contrast-quality index, and average values of discrete entropy results, which are more than 32.30, 94.0%, 0.98.9%, and 7.4, respectively. In a nutshell, AEHO can be an efficient method that can be used to tackle several image processing problems.

In this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal, and virtual shaving before the skin lesion segmentation. The Non-Local Means (NLM) filter with lowest Blind Reference less Image Spatial Quality Evaluator (BRISQUE) score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the denoised image. The Robust Image Contrast Enhancement (RICE) algorithm is used for contrast enhancement, and produces enhanced images with better structural preservation and negligible loss of information. Unsharp masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase congruency–based method in virtual shaving show high similarity with ground truth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework show that the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information, and elimination of artifacts in melanoma images. The output of the proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results, it is found that the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.

Contrast enhancement aims to amplify the visual quality of images by modifying the contrast level because digital images may get distorted by casual acquisition. The article deals with contrast enhancement as an optimization problem and uses the Dragonfly Algorithm (DA) to find the optimal grey-level intensity values. The DA for contrast enhancement uses five control parameters (entropy, number of edges, total intensities of edges, the variance of the probability of occurrence of each grey value, and the number of grey levels) to generate an objective function. An ablation study is also performed to understand how different controlling parameter combinations contribute to determining the optimal solution. The proposed approach considers 24 grey-scale images from the Kodak dataset and metrics as Peak Signal-to-Noise Ratio (PSNR), Visual Information Fidelity (VIF), and Structural Similarity Index Measure (SSIM) to verify the output's performance. The PSNR, VIF, and SSIM values in the experiments are 30.87, 0.7451, and 0.9523, respectively. The experimental observations reveal that the proposed DA-based image contrast enhancement produces high-quality images from its low-contrast counterparts. Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. The Python implementation of the proposed approach is available in this Github repository.

Mobile phone technology has led to implementation of portable and inexpensive microscopes. Light-emitting diode (LED) array microscopes support various multicontrast imaging by flexible illumination patterns of the LED array that can be achieved without changing the optical components of the microscope. Here, we demonstrate a mobile-phone-based LED array microscope to realize multimodal imaging with bright-field, dark-field, differential phase-contrast, and Rheinberg illuminations using as few as 37 LED bulbs. Using this microscope, we obtained high-contrast images of living cells. Furthermore, by changing the color combinations of Rheinberg illumination, we were able to obtain images of living chromatic structures with enhanced or diminished contrast. This technique is expected to be a foundation for high-contrast microscopy used in modern field studies.

Image decolorization is an image pre-processing step which is widely used in image analysis, computer vision, and printing applications. The most commonly used methods give each color channel (e.g., the R component in RGB format, or the Y component of an image in CIE-XYZ format) a constant weight without considering image content. This approach is simple and fast, but it may cause significant information loss when images contain too many isoluminant colors. In this paper, we propose a new method which is not only efficient, but also can preserve a higher level of image contrast and detail than the traditional methods. It uses the information from the cumulative distribution function (CDF) of the information in each color channel to compute a weight for each pixel in each color channel. Then, these weights are used to combine the three color channels (red, green, and blue) to obtain the final grayscale value. The algorithm works in RGB color space directly without any color conversion. In order to evaluate the proposed algorithm objectively, two new metrics are also developed. Experimental results show that the proposed algorithm can run as efficiently as the traditional methods and obtain the best overall performance across four different metrics.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death. Recent studies have underlined the importance of non-contrast-enhanced chest CT scans not only for emphysema progression quantification, but for correlation with clinical outcomes as well. As about 40 percent of the 300 million CT scans per year are contrast-enhanced, no proper emphysema quantification is available in a one-stop-shop approach for patients with known or newly diagnosed COPD. Since the introduction of spectral imaging (e.g., dual-energy CT scanners), it has been possible to create virtual non-contrast-enhanced images (VNC) from contrast-enhanced images, making it theoretically possible to offer proper COPD imaging despite contrast enhancing. This study is aimed towards investigating whether these VNC images are comparable to true non-contrast-enhanced images (TNC), thereby reducing the radiation exposure of patients and usage of resources in hospitals. In total, 100 COPD patients with two scans, one with (VNC) and one without contrast media (TNC), within 8 weeks or less obtained by a spectral CT using dual-layer technology, were included in this retrospective study. TNC and VNC were compared according to their voxel-density histograms. While the comparison showed significant differences in the low attenuated volumes (LAVs) of TNC and VNC regarding the emphysema threshold of −950 Houndsfield Units (HU), the 15th and 10th percentiles of the LAVs used as a proxy for pre-emphysema were comparable. Upon further investigation, the threshold-based LAVs (−950 HU) of TNC and VNC were comparable in patients with a water equivalent diameter (DW) below 270 mm. The study concludes that VNC imaging may be a viable option for assessing emphysema progression in COPD patients, particularly those with a normal body mass index (BMI). Further, pre-emphysema was generally comparable between TNC and VNC. This approach could potentially reduce radiation exposure and hospital resources by making additional TNC scans obsolete.

In recent years, Unmanned Aerial Vehicles (UAVs) have become popular tools in mapping applications. In such applications, the image motion, bad lighting effects, and poor texture all directly affect the quality of the derived tie points, which in turn imposes constraints on image extraction and may lead to a low accuracy point cloud. This paper proposes a contrast enhancement technique to improve the accuracy of a photogrammetric model created using UAV images. The luminance component (Y) in the YIQ color space is normalized using the sigmoid function, and the low contrast images are enhanced using the Contrast-Limited Adaptive Histogram Equalization (CLAHE) on the luminosity component. To evaluate the proposed method, three-dimensional models were created using images acquired by the Phantom 4 Pro UAV in three distinct places and at altitudes of 20, 40, 60, 80, and 90 meters. The results showed that enhancing the contrast of images increased the number of tie points and reduced reprojection error by approximately 10%. It also improved the resolution of the digital elevation model by approximately 2cm/pixel while greatly improving the texture and quality with respect to that developed using the original images.

Micro-polarizer array (MPA) is the core optical component of the Division of Focal-Plane (DoFP) imaging system, and its design is very important to the system’s performance. Traditional design methods rely on theoretical analysis and simulation, which is complicated and requires designers to have profound theoretical foundations. In order to simplify the design process and improve efficiency, this paper proposes a 2 × 2 MPA reverse-design strategy based on particle swarm optimization (PSO). This strategy uses intelligent algorithms to automatically explore the design space in order to discover MPA structures with optimal optical properties. In addition, the all-pass filter is introduced to the MPA superpixel unit in the design, which effectively reduces the crosstalk and frequency aliasing between pixels. In this study, two MPA models were designed: a traditional MPA and an MPA with an all-pass filter. The Degree of Linear Polarization (DOLP) image contrast is used as the evaluation standard and compared with the traditional MPA; the results show that the contrast of the newly designed traditional MPA image is increased by 21%, and the MPA image with the all-pass filter is significantly increased by 82%. Therefore, the reverse-design method proposed in this paper not only simplifies the design process but also can design an MPA with enhanced optical performance, which has obvious advantages over the traditional method.