Improving Night Time Driving Safety Using Vision-Based Classification Techniques

As the dynamic range of a digital camera is narrower than that of a real scene, the captured image requires a tone curve or contrast correction to reproduce the information in dark regions. Yet, when using a global correction method, such as histogram-based methods and gamma correction, an unintended contrast enhancement in bright regions can result. Thus, a multiscale retinex algorithm using Gaussian filters was already proposed to enhance the local contrast of a captured image using the ratio between the intensities of an arbitrary pixel in the captured image and its surrounding pixels. The intensity of the surrounding pixels is estimated using Gaussian filters and weights for each filter, and to obtain better results, these Gaussian filters and weights are adjusted in relation to the captured image. Nonetheless, this adjustment is currently a subjective process, as no method has yet been developed for optimizing the Gaussian filters and weights according to the captured image. Therefore, this article proposes local contrast enhancement based on an adaptive multiscale retinex using a Gaussian filter set adapted to the input image. First, the weight of the largest Gaussian filter is determined using the local contrast ratio from the intensity distribution of the input image. The other Gaussian filters and weights for each Gaussian filter in the multiscale retinex are then determined using a visual contrast measure and the maximum color difference of the color patches in the Macbeth color checker. The visual contrast measure is obtained based on the product of the local standard deviation and locally averaged luminance of the image. Meanwhile, to evaluate the halo artifacts generated in large uniform regions that abut to form a high contrast edge, the artifacts are evaluated based on the maximum color difference between each color of the pixels in a patch in the Macbeth color and the averaged color in CIELAB standard color space. When considering the color difference for halo artifacts, the parameters for the Gaussian filters and weights representing a higher visual contrast measure are determined using test images. In addition, to reduce the induced graying-out, the chroma of the resulting image is compensated by preserving the chroma ratio of the input image based on the maximum chroma values of the sRGB color gamut in the lightness–chroma plane. In experiments, the proposed method is shown to improve the local contrast and saturation in a natural way.

This paper proposes a novel approach to reduce the operation time in the Multi-scale Retinex (MSR) while preserving the result image quality. Existing MSR algorithms take much time because they have many arithmetic operations. The proposed algorithm uses MSR weights selection algorithm to determine the important Single-scale Retinex (SSR) among the small, middle, and large SSRs. To reduce the operation time, we reduce the MSR weights selection arithmetic operation but have similar weights. And then, we skip SSR operations to reduce the operation time except dominant SSR when one SSR weight is dominant. Finally, we compare the MSR result image with the original image to reduce graying out. In the experiment, the proposed algorithm has 25%, 13% and 204% performance increment compared with benchmark method1, 2 and 3 and similar result image quality compared with conventional methods.

With the ability of imaging the temperature distribution of body, infrared imaging is promising in diagnostication and prognostication of diseases. However the poor quality of the raw original infrared images prevented applications and one of the essential problems is the low contrast appearance of the imagined object. In this paper, the image enhancement technique based on the Retinex theory is studied, which is a process that automatically retrieve the visual realism to images. The algorithms, including Frackle-McCann algorithm, McCann99 algorithm, single-scale Retinex algorithm, multi-scale Retinex algorithm and multi-scale Retinex algorithm with color restoration, are experienced to the enhancement of infrared images. The entropy measurements along with the visual inspection were compared and results shown the algorithms based on Retinex theory have the ability in enhancing the infrared image. Out of the algorithms compared, MSRCR demonstrated the best performance.

The images of oral potentially malignant disorders (OPMDs) frequently encounter problems related to visual quality and image distortion, which may lead to serious misdiagnosis and missed diagnosis. This study is to explore the auxiliary effects of different optical image enhancement algorithms on the object detection of OPMDs lesions. Digital images of OPMDs were collected, including white plaques, white stripes, and erosive lesions. The dataset was divided into a training set (6,488 images) and a validation set (2,592 images). Original images were processed using multiscale retinex (MSR), adaptive histogram equalization (AHE), and adaptive contrast enhancement (ACE), respectively. Object detection models based on You Only Look Once version 8 (YOLOv8) were used for lesion detection, and the diagnostic performance was evaluated using 328 images taken at different times. The model performance in the MSR-enhanced image set was superior to that in the original image set, with total accuracy increased for all three lesion types, and the sensitivity of complete correct recognition for complex multi-lesion images improved. Models trained with AHE and ACE preprocessing showed reduced diagnostic performance. Image enhancement algorithms can enhance the visual quality of OPMDs images, and the MSR algorithm is capable of strengthening the object detection ability in the computer vision model. This study provides an approach to reduce the misdiagnosis and missed diagnosis of OPMDs lesions in object detection model.

In view of the defects of the classical Retinex image enhancement algorithm, such as poor texture detail retention, halo and over enhancement, and abrupt tone, this paper proposes a low illumination color image enhancement algorithm based on Gabor filter and retinex theory. Firstly, the brightness I component of the image is extracted in the HSI color space, and then the image with the brightness component is enhanced by MSRCR (the color-recovered retinex algorithm). At the same time, the image is enhanced by SSR (the single-scale retinex algorithm) based on Gabor filter in RGB color space, and the image with better texture and edge details is obtained. Finally, the final enhanced image is obtained by weighted fusion. The algorithm is compared with SSR, MSR (the multi-scale retinex algorithm) and MSRCR. The experimental results show that the image information processed by this algorithm is richer in color, hue and the color is closer to the original image, and it effectively reduces the occurrence of halo and over-enhancement. This algorithm can enhance the image of some low illumination color images, and the visual effect of the enhanced image is relatively peaceful.

This paper describes an efficient image enhancement method based on the multi-scale retinex (MSR) theory for applying to a motion picture pre-processing. The proposed method uses a multi-rate x-y separable Gaussian filter with a polyphase structure and conducts illumination domain MSR composition. Therefore, The processing amount is drastically reduced to 4-order less than that of original MSR. As enhancing dark and bright areas in an image is the main pre-processing purpose, the method modifies an enhancement function so as to improve color rendition of bright areas on an image. Although our approach aims at simple implementation, the proposed method shows similar performance to the original one and better performance than those of histogram equalization approaches.

Sandstorm image enhancement based on YUV space

In this paper, we present a quality enhancement scheme for endoscopic images. Traditional algorithms might be able to enhance the image contrast, but possible over-enhancement also lead to bad overall visual quality which prevents surgeons from accurate examination or operations of instruments in Minimal Invasive Surgery (MIS). Our proposed scheme integrates the well-known retinex algorithm with a pseudo-HDR (High Dynamic Range) synthesis process, designed to compose of three parts: multiscale retinex with gamma correction (MSR-G), local brightness range expansion (brightness diversity), and bilateral-filter-based HDR image fusion. Experiment results demonstrate that the proposed scheme is able to enhance image details and keep the overall visual quality good as well, with respect to other existing methods.

BackgroundThe advancements in unmanned aerial vehicle (UAV) technology have recently emerged as an effective, cost-efficient, and versatile solution for monitoring crop growth with high spatial and temporal precision. This monitoring is usually achieved through the computation of vegetation indices (VIs) from agricultural lands. The VIs are based on the incoming radiance to the camera, which is affected when there is a change in the scene illumination. Such a change will cause a change in the VIs and subsequent measures, e.g., the VI-based chlorophyll-content estimation. In an ideal situation, the results from VIs should be free from the impact of scene illumination and should reflect the true state of the crop’s condition. In this paper, we evaluate the performance of various VIs computed on images taken under sunny, overcast and partially cloudy days. To improve the invariance to the scene illumination, we furthermore evaluated the use of the empirical line method (ELM), which calibrates the drone images using reference panels, and the multi-scale Retinex algorithm, which performs an online calibration based on color constancy. For the assessment, we used the VIs to predict leaf chlorophyll content, which we then compared to field measurements.ResultsThe results show that the ELM worked well when the imaging conditions during the flight were stable but its performance degraded under variable illumination on a partially cloudy day. For leaf chlorophyll content estimation, The r^2 of the multivariant linear model built by VIs were 0.6 and 0.56 for sunny and overcast illumination conditions, respectively. The performance of the ELM-corrected model maintained stability and increased repeatability compared to non-corrected data. The Retinex algorithm effectively dealt with the variable illumination, outperforming the other methods in the estimation of chlorophyll content. The r^2 of the multivariable linear model based on illumination-corrected consistent VIs was 0.61 under the variable illumination condition.ConclusionsOur work indicated the significance of illumination correction in improving the performance of VIs and VI-based estimation of chlorophyll content, particularly in the presence of fluctuating illumination conditions.

In this paper, a fast image enhancement algorithm based on multi-scale retinex in HSV colour model is presented. The proposed algorithm produces the result similar to the one which uses a nonlinear processing in the HSV colour model, but with less computational cost. It uses linear dependencies of RGB colours from the V-component of HSV model. Additionally, to speed up the images processing and enhance the local contrast is suggested to perform multi-scale retinex (MSR) computation only in the low-frequency area obtained by the wavelet transform. Experimental research was performed on more than 100 colour images having non-uniform brightness. Different algorithms based on retinex technology were implemented and their performance was compared. The proposed way of output image colour formation allows reducing processing time by 30%-75%, depending on the image size. The experimental data show that the usage of the wavelet transform in proposed MSR algorithm additionally leads to 2-2.8 times increase in processing speed.

Transport safety assessment plays a huge role in theory and practice of implementation of transport management systems. It should be noted that ensuring the transport safety is inextricably linked to identification of potentially dangerous transport situations and their risk assessment. To better manage transport risks we propose to apply a new integrated approach which allows to take into account the source of the threat, the degree of objects’ protection, kinds of threats, extent of damage in the event of dangerous situations, magnitude of the dangerous situation. This approach is anchored to systematic analysis of causes and conditions of formation of dangerous situations. Also, it provides timely forecasting of their development and consequences. Therefore, it is necessary to develop a new approach to transport risk assessment which will be able to effectively evaluate occurrence of dangerous transport situations. In this paper we present components of transport safety and provide a list of typical objects of transport infrastructure. Transport safety model and algorithm for identification of potentially dangerous transport situation is proposed. Also, we consider a new integrated approach based on transport risk assessment method. It is worth highlighting that the proposed method of transport risk assessment allows taking into consideration the uncertainty linked to the occurrence of dangerous transport situations and their conditions. The specified method can be widely used in modern geographic information systems for analysis of transport emergencies.

Real-time sub-meter location tracking is crucial for vehicle safety in transportation management, especially for large buses and dangerous goods vehicles, but many consumer level GNSS accuracy devices are just around 3-5 meters. This paper introduces a real-time array calibration (RAC) positioning technology, via road test comparison in different scenarios with other two terminals. Through the analysis of multiple indicators, it is found that the RAC terminal has better positioning stability than other terminals in different scenarios and can reach sub-meter accuracy. This technology can be used location based service (LBS) with sub-meter, and it will be widely applied to driver assistance system (DAS), unmanned aerial vehicle (UAV) navigation system and other fields.

While vast numbers of image enhancing algorithms have already been developed, the majority of these algorithms have not been assessed in terms of their visual performance-enhancing effects using militarily relevant scenarios. The goal of this research was to apply a visual performance-based assessment methodology to evaluate six algorithms that were specifically designed to enhance the contrast of digital images. The image enhancing algorithms used in this study included three different histogram equalization algorithms, the Autolevels function, the Recursive Rational Filter technique described in Marsi, Ramponi, and Carrato 1 and the multiscale Retinex algorithm described in Rahman, Jobson and Woodell 2 . The methodology used in the assessment has been developed to acquire objective human visual performance data as a means of evaluating the contrast enhancement algorithms. Objective performance metrics, response time and error rate, were used to compare algorith m enhanced images versus two baseline conditions, original non-enhanced images and contrast-degrade d images. Observers completed a visual search task using a spatial-forced-choice paradigm. Observers searched images for a target (a military vehicle) hidden among foliage and then indicated in which quadrant of the screen the target was located. Response time and percent correct were measured for each observer. Results of the study and future directions are discussed. Keywords: image enhancement, psychophysical experiments, visual search, contrast enhancing algorithm.

The vehicle shadow’s detection and elimination work basically for extracting and tracking the vehicle characteristics, and it also plays a very important role in highway video surveillance and incident detection, affecting the post-processing of video image directly, such as vehicle tracking and speed measurement. On study of Kimmel variational and multi-scale Retinex algorithm to eliminate the vehicle shadow, a Retinex method based on anisotropy edge estimation was proposed, which took shadow edge as outliers and smooth range by range. Experiment shows that the algorithm can avoid halo effect, and shadows can be removed. It indicates this algorithm can actually be applied to the highway video surveillance and incident detection system.

Photoacoustic Imaging (PAI), as a biomedical imaging technology, boasts remarkable features containing exceptional resolution and outstanding contrast. However, when lasers propagate through biological tissues, its energy rapidly attenuates with distance. Consequently, the photoacoustic (PA) signal becomes weak, resulting in worse contrast and resolution of the PA image. To address this challenge, we develop the multi-scale retinex (MSR) algorithm for enhancing PA images. Subsequently, the MSR algorithm was utilized in animal and human experiments. And the enhanced images were quantitative analyzed by information entropy, contrast and brightness. The results indicate a significant improvement in both contrast and resolution of the PA images enhanced by the MSR algorithm.