Video Image Enhancement Research Articles

Research on image enhancement algorithm for the monitoring system in coal mine hoist

As the mine hoist monitors video images with poor light, low brightness, heavy dust, and low contrast, the monitoring video images are not conducive to monitoring. They cannot meet the needs of applications. Based on actual video surveillance data, this paper proposes a dark channel prior (DCP) method integrated with a guided image filter video image enhancement algorithm. Firstly, we analyzed the characteristics of the mine hoist system’s video images. Then, the DCP technique was used to enhance the video images. A guided image filter algorithm was introduced to ensure that the video has more clarity and visual impact. Comparing the suggested method to the other four algorithms, it performed better both subjectively and objectively than the algorithms mentioned above. Experiments demonstrate that the proposed technique can successfully improve the entire clarity and contrast of video images while avoiding the over-enhancement of bright areas close to the light source, meeting the practical application requirements of video surveillance.

Correction: Lopez-Vazquez et al. Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories. Sensors 2020, 20, 726

The authors wish to correct the following error in the original paper [...].

Application of Video Processing Technology Based on Diffusion Equation Model in Basketball Analysis

Video event detection and annotation work is an important content of video analysis and the basis of video content retrieval. Basketball is one of the most popular types of sports. Event detection and labeling of basketball videos can help viewers quickly locate events of interest and meet retrieval needs. This paper studies the application of anisotropic diffusion in video image smoothing, denoising, and enhancement. An improved form of anisotropic diffusion that can be used for video image enhancement is analyzed. This paper studies the anisotropic diffusion method for coherent speckle noise removal and proposes a video image denoising method that combines anisotropic diffusion and stationary wavelet transform. This paper proposes an anisotropic diffusion method based on visual characteristics, which adds a factor of video image detail while smoothing, and improves the visual effect of diffusion. This article discusses how to apply anisotropic diffusion methods and ideas to video image segmentation. We introduced the classic watershed segmentation algorithm and used forward-backward diffusion to process video images to reduce oversegmentation, introduced the active contour model and its improved GVF Snake, and analyzed the idea of how to use anisotropic diffusion and improve the GVF Snake model to get a new GGVF Snake model. In the study of basketball segmentation of close-up shots, we propose an improved Hough transform method based on a variable direction filter, which can effectively extract the center and radius of the basketball. The algorithm has good robustness to basketball partial occlusion and motion blur. In the basketball segmentation research of the perspective shot, the commonly used object segmentation method based on the change area detection is very sensitive to noise and requires the object not to move too fast. In order to correct the basketball segmentation deviation caused by the video noise and the fast basketball movement, we make corrections based on the peak characteristics of the edge gradient. At the same time, the internal and external energy calculation methods of the traditional active contour model are improved, and the judgment standard of the regional optimal solution and segmentation validity is further established. In the basketball tracking research, an improved block matching method is proposed. On the one hand, in order to overcome the influence of basketball’s own rotation, this article establishes a matching criterion that has nothing to do with the location of the area. On the other hand, this article improves the diamond motion search path based on the basketball’s motion correlation and center offset characteristics to reduce the number of searches and improve the tracking speed.

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior.

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.

Low Illumination Video Image Enhancement

Due to weather conditions, brightness conditions, capture equipment and other factors, leads to video unclear or even abnormally confused, which is not conducive to monitoring, and can not meet the needs of applications. Based on the actual data of night video surveillance, this paper proposes a new low illumination video image enhancement algorithm, which overcomes the existing problems. We analyze the characteristics of low illumination video image, and use HSV color space instead of traditional RGB space to enhance the robustness of video contrast and color distortion. At the same time, we use wavelet image fusion to highlight the details of video image, so the enhanced video has higher clarity and visual effect. Compared with other four algorithms, the proposed algorithm outperforms the above algorithms in subjective evaluation and objective evaluation. At the same time, compared with other algorithms, the proposed algorithm has faster processing time for each frame. Experiments show that the algorithm can effectively improve the overall brightness and contrast of video images, and avoid the over-enhancement of bright areas near the light source, which can meet the practical application requirements of video surveillance.

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories.

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

Real time video image enhancement approach using particle swarm optimisation technique with adaptive cumulative distribution function based histogram equalization

Recent years, videos are playing an important role in different applications such as pattern recognition, security purpose, news analysis, weather digest, and video browser and so on. Due to the importance of video applications, the quality of the video must be improved for making effective results in video analysis process. This paper introduces the particle optimization with adaptive cumulative distribution based histogram enhancement technique (PACDHE) for improving the video quality. Initially the videos are collected, each incoming frame has been analyzed and noise present in the video frame is eliminated by applying the non-divisional median filter. After that, quality of video is enhanced iteratively by examining each pixel present in video frames using the optimized fitness and cumulative distribution function. This process is repeated continuously until to enhance the video frames contrast and quality. Then the performance of the system is analyzed by using CV online video database and the efficiency is examined in terms of peak signal to noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Entropy. The experimental results of PSO are compared with genetic algorithm based approach and found that PSO outperforms the GA approach and the existing histogram equalization approach and the existing histogram equalization approaches.

A method of video image enhancement

A new method of image enhancement that allows improving the quality of required image details is proposed. To enhance local contrasts, a special transformation function is submitted. By the example of enhancement of a video-shot image of a car, we show the efficiency of the proposed technique in comparison with known methods of contrast enhancement.

P-177: An Adaptive Image Enhancement Architecture for Mobile System

We have developed an adaptive image enhancement architecture composed of new algorithm and new circuit for mobile system. The algorithm utilizes the scene change detection and realizes flicker-less enhancement of video images by a time-domain filter. The circuit is built in the LCD driver and enables real time enhancement of QVGA 30fps images with only 2.5mW power consumption.

Surgical skill facilitation in videoscopic open surgery.

The operating room (OR) was traditionally characterized as a closed environment, in which the view of the operative field was available to the surgeon and assistant only. In laparoscopy, integration of technology into the surgical theatre has transformed surgical procedures into minimally invasive events, with viewing of the surgical field using endoscopic cameras. Similar technical advances to the open surgical environment will allow visualization and coordination of finer surgical maneuvers on standard video monitors. The objective of this study was to develop optimal protocols for performing basic open surgical maneuvers without direct viewing of the operating field, instead watching a monitor that displays the image of the surgical field captured by an endoscopic camera. The AESOP robotic arm and Alpha Virtual Port (Computer Motion, Goleta, California) were used to hold the endoscopic camera in different positions relative to the surgeon and the operative table. The surgeons conducting the study evaluated six such different setups. Based on the average time to complete the task in each of these setups and the ease of adaptation to the new working conditions, we concluded that at least one of these setups could be translated into the OR. The advantages of integrating video image enhancement over classical open surgery (OS) are that the surgical field can be magnified to perform finer maneuvers, and to share views of the surgical field with additional clinicians and trainees.

Dense gas simulation flows over rough surfaces

Mixing within a gravity current head, passing over a rough surface, is assessed using a two-dimensional quantitative full-field flow visualization technique. The method combines salt-water modelling, laser-induced dye fluorescence and video image enhancement. The mixing mechanisms that occur internal to the head structure are shown to play a major role in the overall dilution process and to be significantly affected by surface roughness. The effect of surface roughness is consequential for the accurate prediction of heavier-than-air gas dispersions, for instance. Flows with Reynolds numbers of 500, 1000 and 1500 and density differences of 0.5, 1.0 and 1.5% are considered. Results are presented for flows over two surface types — smooth and rough with a relative roughness element height of 0.35 and frontal obstruction of 50% based on roughness element diameter.

In situ analysis of neuronal dynamics and positional cues in the patterning of nerve connections

Recently developed imaging techniques permit individual cells to be uniquely labeled and followed over time as development proceeds in intact vertebrate embryos. Small groups of cells in the developing eye rudiment of the frog Xenopus have been labeled with the vital dyes DiI, lysinated fluorescein dextran (LFD) or lysinated rhodamine dextran (LRD). Individual optic axons and their growth cones were clearly visible in the intact living animal using confocal microscopy or epifluorescence microscopy with a low light level video camera and computer-based video image enhancement. To follow the dynamics of single optic nerve fiber terminal arborizations, small groups of cells, or even single retinal ganglion cells, were labeled with DiI, and the resulting labeled optic nerve fibers were imaged using a confocal microscope. The images show a profound alteration in morphology from day to day, demonstrating that optic nerve terminal arborizations are dynamic structures constantly extending and retracting branches. To follow the topography of the developing projection and analyze the cues that guide its formation, small groups of eyebud cells from LFD- and LRD-labeled donor embryos were grafted to an unlabeled host in either a location equivalent to that from which they had been removed (homotopic grafts) or a non-equivalent location (heterotopic grafts). Axons from homotopic grafts projected to the tectum as expected from the adult topography of the retinotectal projection. Dorsoventral topography was present from the time that the optic nerve fibers were observable in the tectum, in agreement with previous work. Nasotemporal topography was subtle or absent for the first few days, and then slowly refined. The importance of positional cues was tested by performing heterotopic eyebud grafts, in which the labeled eyebud cells are grafted to inappropriate places in the host eyebud. The heterotopic grafts appeared to integrate with the ectopic site in the eyebud in a functional manner. They should, therefore, project to the tectum together with their new neighbors if neighbor interactions or activity-based cues were of primary importance in the initial patterning of the map. However, the experiments showed that the axons from heterotopic grafts always behaved in a fashion appropriate to their position of origin in the donor, regardless of their final position in the host. These observations indicate that small groups of eyebud cells (as small as a single cell) possess positional information that plays a dominant role in guiding the optic nerve fibers to their target sites in the tectum.

Video microscopy of living cells and dynamic molecular assemblies

The light microscope coupled to appropriate video equipment and image-processing computers now yields images with hitherto unobtainable clarity and resolution. Objects far smaller than the limit of resolution can be visualized to reveal their dynamic behavior in several contrast modes. This paper reviews the role of video contrast enhancement which makes possible the optimal use of these hidden, inherent capabilities of the light microscope. Analog and digital video image enhancement as well as intensifier video cameras are used to bring out microscopic and submicroscopic features of living cells and dynamic molecular assemblies unsuspected before the application of video microscopy.

Digital enhancement of video images for NDT

Digital enhancement of video images for NDT

Digital enhancement of video images for NDT

Due to recent advances in micro-electronics, many digital image enhancement techniques can now be readily applied at standard video-rates using commercially available hardware. These techniques are being increasingly applied in the field of nondestructive testing, in particular to methods that generate TV-type video images, such as real-time radiography and pulse-video thermography. The latest techniques used by the National NDT Centre at Harwell are described and are illustrated by examples from three application areas.