Non-uniform Lighting Research Articles

Adltformer Team-Training with Detr: Enhancing Cattle Detection in Non-Ideal Lighting Conditions Through Adaptive Image Enhancement

This study proposes an image enhancement detection technique based on Adltformer (Adaptive dynamic learning transformer) team-training with Detr (Detection transformer) to improve model accuracy in suboptimal conditions, addressing the challenge of detecting cattle in real pastures under complex lighting conditions—including backlighting, non-uniform lighting, and low light. This often results in the loss of image details and structural information, color distortion, and noise artifacts, thereby compromising the visual quality of captured images and reducing model accuracy. To train the Adltformer enhancement model, the day-to-night image synthesis (DTN-Synthesis) algorithm generates low-light image pairs that are precisely aligned with normal light images and include controlled noise levels. The Adltformer and Detr team-training (AT-Detr) method is employed to preprocess the low-light cattle dataset for image enhancement, ensuring that the enhanced images are more compatible with the requirements of machine vision systems. The experimental results demonstrate that the AT-Detr algorithm achieves superior detection accuracy, with comparable runtime and model complexity, reaching 97.5% accuracy under challenging illumination conditions, outperforming both Detr alone and sequential image enhancement followed by Detr. This approach provides both theoretical justification and practical applicability for detecting cattle under challenging conditions in real-world farming environments.

UPT-Flow: Multi-scale transformer-guided normalizing flow for low-light image enhancement

Low-light images often suffer from information loss and RGB value degradation due to extremely low or nonuniform lighting conditions. Many existing methods primarily focus on optimizing the appearance distance between the enhanced image and the normal-light image, while neglecting the explicit modeling of information loss regions or incorrect information points in low-light images. To address this, this paper proposes an Unbalanced Points-guided multi-scale Transformer-based conditional normalizing Flow (UPT-Flow) for low-light image enhancement. We design an unbalanced point map prior based on the differences in the proportion of RGB values for each pixel in the image, which is used to modify traditional self-attention and mitigate the negative effects of areas with information distortion in the attention calculation. The Multi-Scale Transformer (MSFormer) is composed of several global-local transformer blocks, which encode rich global contextual information and local fine-grained details for conditional normalizing flow. In the invertible network of flow, we design cross-coupling conditional affine layers based on channel and spatial attention, enhancing the expressive power of a single flow step. Without bells and whistles, extensive experiments on low-light image enhancement, night traffic monitoring enhancement, low-light object detection, and nighttime image segmentation have demonstrated that our proposed method achieves state-of-the-art performance across a variety of real-world scenes. The code and pre-trained models will be available at https://github.com/NJUPT-IPR-XuLintao/UPT-Flow.

Underwater image restoration via attenuated incident optical model and background segmentation

Underwater images typically exhibit low quality due to complex imaging environments, which impede the development of the Space-Air-Ground-Sea Integrated Network (SAGSIN). Existing physical models often ignore the light absorption and attenuation properties of water, making them incapable of resolving details and resulting in low contrast. To address this issue, we propose the attenuated incident optical model and combine it with a background segmentation technique for underwater image restoration. Specifically, we first utilize the features to distinguish the foreground region of the image from the background region. Subsequently, we introduce a background light layer to improve the underwater imaging model and account for the effects of non-uniform incident light. Afterward, we employ a new maximum reflection prior in the estimation of the background light layer to achieve restoration of the foreground region. Meanwhile, the contrast of the background region is enhanced by stretching the saturation and brightness components. Extensive experiments conducted on four underwater image datasets, using both classical and state-of-the-art (SOTA) algorithms, demonstrate that our method not only successfully restores textures and details but is also beneficial for processing images under non-uniform lighting conditions.

Hetero-associative Memory Based New Iraqi License Plate Recognition

نتيجة للتطورات الأخيرة في أبحاث الطرق السريعة بالإضافة إلى زيادة استخدام المركبات، كان هناك اهتمام كبير بنظام النقل الذكي الأكثر حداثة وفعالية ودقة (ITS) في مجال رؤية الكمبيوتر أو معالجة الصور الرقمية، يلعب تحديد كائنات معينة في صورة دورًا مهمًا في إنشاء صورة شاملة. هناك تحدٍ مرتبط بالتعرف على لوحة ترخيص السيارة (VLPR) بسبب الاختلاف في وجهة النظر، والتنسيقات المتعددة، وظروف الإضاءة غير الموحدة في وقت الحصول على الصورة والشكل واللون، بالإضافة إلى الصعوبات مثل ضعف دقة الصورة ، الصورة الباهتة ، الإضاءة السيئة، التباين المنخفض، يجب التغلب عليها. اقترحت هذه الورقة نموذجًا باستخدام تعديل الذاكرة الترابطية ثنائية الاتجاه (MBAM)، وهي نوع واحد من الذاكرة الترابطية غير المتجانسة، وتعمل MBAM على مرحلتين)مرحلتي التعلم والتقارب) للتعرف على اللوحة، ويمكن لهذا النموذج المقترح التغلب على تلك الصعوبات بسبب قدرة الذاكرة الترابطية لـ MBAM على قبول الضوضاء وتمييز الصور المشوهة، وكذلك سرعة عملية الحساب نظرًا لصغر حجم الشبكة. نتيجة دقة تحديد منطقة اللوحة هي 99.6٪، ودقة تجزئة الأحرف 98٪، والدقة المحققة للتعرف على الأحرف هي100 ٪ في ظروف مختلفة.

Improving the evidential value of low-quality face images with aggregation of deep neural network embeddings

In forensic facial comparison, questioned-source images are usually captured in uncontrolled environments, with non-uniform lighting, and from non-cooperative subjects. The poor quality of such material usually compromises their value as evidence in legal proceedings. On the other hand, in forensic casework, multiple images of the person of interest are usually available. In this paper, we propose to aggregate deep neural network embeddings from various images of the same person to improve the performance in forensic comparison of facial images. We observe significant performance improvements, especially for low-quality images. Further improvements are obtained by aggregating embeddings of more images and by applying quality-weighted aggregation. We demonstrate the benefits of this approach in forensic evaluation settings with the development and validation of common-source likelihood ratio systems and report improvements in Cllr both for CCTV images and for social media images.

Light consistency correction for the liquid crystal tunable filter hyperspectral imaging system

In hyperspectral images, every pixel encompasses continuous spectral information. Compared with traditional colorimeters, using hyperspectral imaging systems (HIS) for fabric color measurement can result in obtaining richer color information. However, measuring fabric colors with liquid crystal tunable filter HIS can lead to challenges related to light consistency. In this paper, we adopted an innovative approach, integrating gradient boosted decision trees with a sliding window algorithm to develop a uniformity calibration model addressing the illumination uniformity issue. To address the consistency issues across various light sources, we further adopted a deep neural network (DNN) model to correct the reflectance measurements under different light sources. Subsequently, this model was merged with the uniformity calibration model to form a light-consistency correction model. Through calibration, we successfully reduced the color difference of the corrected samples from 3.636 to 0.854, an enhancement of 76.51%. This means that after calibration we can achieve consistency in fabric color measurements under nonuniform lighting and different light sources.

Implementation of a High-Accuracy Neural Network-Based Pupil Detection System for Real-Time and Real-World Applications.

In this paper, the implementation of a new pupil detection system based on artificial intelligence techniques suitable for real-time and real-word applications is presented. The proposed AI-based pupil detection system uses a classifier implemented with slim-type neural networks, with its classes being defined according to the possible positions of the pupil within the eye image. In order to reduce the complexity of the neural network, a new parallel architecture is used in which two independent classifiers deliver the pupil center coordinates. The training, testing, and validation of the proposed system were performed using almost 40,000 eye images with a resolution of 320 × 240 pixels and coming from 20 different databases, with a high degree of generality. The experimental results show a detection rate of 96.29% at five pixels with a standard deviation of 3.38 pixels for all eye images from all databases and a processing speed of 100 frames/s. These results indicate both high accuracy and high processing speed, and they allow us to use the proposed solution for different real-time applications in variable and non-uniform lighting conditions, in fields such as assistive technology to communicate with neuromotor-disabled patients by using eye typing, in computer gaming, and in the automotive industry for increasing traffic safety by monitoring the driver's cognitive state.

Segmentation and deep learning to digitalize the kinematics of flow-type landslides

Flow-type landslides, including subaerial and submarine debris flows, have poor spatiotemporal predictability. Therefore, researchers rely heavily on experimental evidence in revealing complex flow mechanisms and evaluating theoretical models. To measure the velocity field of experimental flows, conventional image analysis tools for measuring soil deformation and hydraulics have been borrowed. However, these tools were not developed for capturing the kinematics of fast-moving soil–water mixtures over complex terrain under non-uniform lighting conditions. In this study, a new framework based on deep learning was used to automatically digitalize the kinematics of experimental flow-type landslides. Captured images were broken into sequences and binarized using a fully convolutional neural network (FCNN). The proposed framework was demonstrated to outperform classic image processing algorithms (e.g., particle image velocimetry, trainable Weka segmentation, and thresholding algorithms) over a wide range of experimental conditions. The FCNN model was even able to process images from consumer-grade cameras under complex shadow, light, and boundary conditions. This feature is most useful for field-scale experimentation. With fewer than 15 annotated training images, the FCNN digitalized experimental flows with an accuracy of 97% in semantic segmentation.

Empathic Lighting Design for Healthcare Environments

Light has the powerful capacity to generate a certain quality and atmosphere within a room. However, lighting design specification is often only characterized via quantitative metrics. In healthcare institutions, such as care homes, hospitals and rehabilitation centres, the use of lighting design may support a positive atmosphere, and potentially also support the health and wellbeing of patients. When people are hospitalised, they are in a difficult and often stressful situation, in which they meet nurses, physicians and relatives within new, unfamiliar surroundings. These situations call for supportive architectural spaces with atmospheres that help people relax and feel safe. Nevertheless, these spaces often lack a lighting design that supports this intent. The objective of this study is to implement two different lighting scenarios and investigate how they affect patients in a psychiatric affective healthcare unit at Copenhagen Psychiatric Centre. Two different lighting scenarios were implemented and tested in a SW-facing group therapy room, during one winter period (November 2022–March 2023). In all, the study included 12 patients. Through questionnaires, the patients were asked how they perceived the luminous environments of two lighting scenarios, one in the existing healthcare lighting vs. a new lighting setup. The patients indicated their preferences and how the lighting design affected and supported heir therapeutic session. The two lighting scenarios are: a) Four high-positioned LED-luminaires (H=2,65 m) in the ceiling; and b) Two low-positioned LED-luminaires suspended above the meeting table (H=1,50 m). The results of the survey showed that patients overall preferred the low-positioned, non-uniform lighting, which scored a total of 60 points in the semantic analysis, compared to 35 points for the high-positioned, uniform lighting. In addition, from a sustainability viewpoint, energy consumption for the low-positioned lighting scenario was only 21 W, compared to 140 W for the high-positioned lighting scenario, providing an 85% reduction in total energy consumption.

Topological charge identification of superimposed orbital angular momentum beams under turbulence using an attention mechanism.

Due to the unique features, orbital angular momentum (OAM) beams have been widely explored for different applications. Accurate determination of the topological charge (TC) of these beams is crucial for their optimal utilization. In this paper, we propose a method that combines adaptive image processing techniques with a simple, parameter-free attention module (SimAM) based convolutional neural network to accurately identify the TC of high-order superimposed OAM beams. Experimental results demonstrate that under the combined influence of non-extreme light intensity and turbulence, it can achieve >95% identification accuracy of TCs ranging from ±1 to ±40. Moreover, even under partial-pattern-missing conditions, our method maintains an accuracy rate of over 80%. Compared with traditional attention mechanisms, SimAM does not require additional network design, significantly reducing the computational costs. Our approach showcases remarkable efficiency, robustness, and cost-effectiveness, making it adaptable to challenging factors such as non-uniform lighting and partially occluded light paths. This research provides a new direction for recognizing OAM modes with valuable implications for the future of communication systems.

Multiple linear regression based illumination normalization for non-uniform light image thresholding

Thresholding-based two-class image binarization is one of the simplest and most popular approaches. However, the performance of global thresholding degrades under non-uniform lighting conditions. Local thresholding methods are widely used for binarizing uneven light images. The appropriate choice of the initial size of the window and designing the bimodal criteria function are the most challenging tasks for the local thresholding approaches. Therefore, to make it simpler, in this work, a novel approach is developed to improve the efficacy of binarizing any uneven light images. To begin with, a two stage approach is developed to extract valid training sample points from the uneven light images for estimating the illumination surface. In addition, the Multiple-Linear-Regression (MLR) method is applied on the extracted training sample points to estimate the illumination surface. Furthermore, the estimated illumination surface is used to normalize the non-uniform light of the image to binarize the image using Otsu’s global thresholding. The proposed approach is validated on different variants of uneven light images and with six different states of art uneven light image binarization approaches. It is observed from the simulations that the performance of the proposed approach outperforms the other approaches in qualitative as well as quantitative measures. Further, the binarization of uneven document image methods are not effective on object background binarization of uneven images. The proposed approach has the average F-Measure (F1) score of 0.98, average Jaccard Index (JI) score of 0.97, average Percentage of Misclassification Error (PME) score of 1.10 and the computational complexity of 2.64 sec.

Monitoring and Diagnostic Method for Maintaining the Operation Parameters of a Solar Module under Non-Uniform Illumination

The most well-known methods for diagnosing solar modules are considered; it is shown that the described diagnostic methods do not allow identifying defects that develop over a long time. The operation of photovoltaic cells under non-uniform illumination is analyzed. It has been shown that the operation of solar modules under non-uniform illumination can lead to thermal damage to semiconductor materials and deterioration in the module energy characteristics due to the fact that some of the photovoltaic cells located at reduced energy illumination become a parasitic load for more illuminated photovoltaic cells included in such module. An analytical equation has been obtained in explicit form for the full current-voltage characteristic of a photovoltaic cell using the Lambert W-function in forward and reverse bias, which makes it possible to study the connections of photovoltaic cells as a part of a solar module under non-uniform energy illumination, where the reverse branch of the current-voltage characteristic is important. The analytical equation makes it possible to predict the behavior of photovoltaic cells and solar modules composed of them under various operating modes, including identifying the degradation process. An electronic measuring device has been developed and manufactured that makes it possible to measure the light and dark current-voltage characteristics of a photovoltaic cell, including the negative branch, in automatic mode. This reduces measurement time and the influence of external experimental conditions when obtaining the current-voltage characteristics of the photovoltaic cell. A method for constructing solar modules from photovoltaic cells under conditions of their operation under non-uniform lighting has been proposed. As a result of such selection, reliability is increased and an increase in the efficiency of the solar module in shading mode, composed of series-connected photovoltaic cells selected in this way, is achieved.

An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter

Underwater images are usually suffering from the issues of quality degradation, such as low contrast due to blurring details, color deviations, non-uniform lighting, and noise. Since last few decades, many researches are undergoing for restoration and enhancement for degraded underwater images. In this paper, we proposed a novel algorithm using modified anisotropic diffusion filter with dynamic color balancing strategy. This proposed algorithm performs based on an employing effective noise reduction as well as edge preserving technique with dynamic color correction to make uniform lighting and minimize the speckle noise. Furthermore, reanalyze the contributions and limitations of existing underwater image restoration and enhancement methods. Finally, in this research provided the detailed objective evaluations and compared with the various underwater scenarios for above said challenges also made subjective studies, which shows that our proposed method will improve the quality of the image and significantly enhanced the image.

Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory

To address the issues of fuzzy scene details, reduced definition, and poor visibility in images captured under non-uniform lighting conditions, this paper presents an algorithm for effectively enhancing such images. Firstly, an adaptive color balance method is employed to address the color differences in low-light images, ensuring a more uniform color distribution and yielding a low-light image with improved color consistency. Subsequently, the image obtained is transformed from the RGB space to the HSV space, wherein the multi-scale Gaussian function is utilized in conjunction with the Retinex theory to accurately extract the lighting components and reflection components. To further enhance the image quality, the lighting components are categorized into high-light areas and low-light areas based on their pixel mean values. The low-light areas undergo improvement through an enhanced adaptive gamma correction algorithm, while the high-light areas are enhanced using the Weber–Fechner law for optimal results. Then, each block area of the image is weighted and fused, leading to its conversion back to the RGB space. And a multi-scale detail enhancement algorithm is utilized to further enhance image details. Through comprehensive experiments comparing various methods based on subjective visual perception and objective quality metrics, the algorithm proposed in this paper convincingly demonstrates its ability to effectively enhance the brightness of non-uniformly illuminated areas. Moreover, the algorithm successfully retains details in high-light regions while minimizing the impact of non-uniform illumination on the overall image quality.

Barcode positioning method based on line detection under non-uniform illumination

The positioning problem of the barcode is easily affected by factors such as uneven illumination and complex background. Based on the EDLines line detection algorithm, a positioning algorithm that can deal with the barcode positioning problem under non-uniform lighting conditions is proposed. First, the EDLines algorithm is used to detect the straight-line segment of the image, and the midpoint coordinates of all line segments are extracted. Since the midpoint coordinates near the barcode are relatively densely distributed, the density-based HDBSCAN point clustering algorithm is used for all midpoint coordinates for density clustering. Then a filtering strategy based on the consistency of line segment direction is proposed to preliminarily filter the clustering results, and one or more point clustering results that may belong to the barcode area are obtained. Then the RANSAC algorithm is used to perform line fitting on the point clustering results. The accuracy of the straight-line fitting results is judged by direction consistency screening, and the intersection of the fitted straight line and the straight-line segment to which the centroid belongs is calculated for the results that meet the expectations, and the width of the barcode area is obtained. Finally, the straight-line segment obtained by screening is calculated. The average length and the height of the barcode area are got, to accurately locate the barcode position. Compared with the previous algorithms on the public barcode dataset and the self-made barcode dataset, the algorithm has good positioning accuracy and running speed.

A reflectance re-weighted Retinex model for non-uniform and low-light image enhancement

Image enhancement is a fundamental low-level task of significant importance that can directly affect high-level image processing tasks. Although various methods have been proposed to enhance images, the effectiveness of current methods deteriorates significantly under non-uniform lighting. Since the brightness may vary dramatically in different regions of real-world photos, current methods hardly achieve a good balance between enhancing low-light regions and retaining normal-light regions in the same image. Consequently, either the low-light regions are under-enhanced or the normal-light regions are over-enhanced, while at the same time, color distortion and artifacts are frequently found. To overcome this shortcoming, we propose a robust Retinex-based model with reflectance map re-weighting that can improve the brightness level of the low-light image and re-balance the brightness concurrently. We introduce an alternating scheme to solve our proposed model, in which the illumination map, reflectance map, and weighting map are updated iteratively. By utilizing the regularization terms, the noise is well-suppressed during the process. An initialization scheme for the weighting map is also proposed to make our model adaptable to a wide range of light conditions. To the best of our knowledge, we are the first to propose a variational model with an explicitly constructed re-weighting prior and the associated weighing map concept for the reflectance map. It can estimate the reflectance map, suppress noise, and re-balance the brightness simultaneously. A series of experimental results on a variety of popular datasets demonstrate the efficacy of our method and its superiority in enhancing real low-light images when compared to other state-of-the-art methods.

Optimization Of Histogram Equation With The Cukcoo Algorithm to Improve Fundus Image Quatlity

This study discusses strategies for identifying Diabetic Retinopathy (DR) using fundus images and the efficiency of image pre-processing techniques to improve their quality. Fundus images in medical image processing often experience problems with non-uniform lighting, low contrast, and noise, thus requiring pre-processing of images to improve their quality. This study evaluates the effectiveness of standard histogram equation techniques and optimized histogram equations with cukkoo optimization in order to choose the best technique to improve fundus image quality to identify DR. The proposed technique to produce better image quality improvements will be tested in several performance metrics, such as NIQE, PSNR, and Entropy. the results of this study, the average PNSR before optimization was 50,8, whereas after optimization it became 49,8239. The average entropy before optimization is 4.5514, while after optimization it becomes 3.8577. The average NIQE before optimization was 3,4046, while after optimization it was 4,73. In general, the results of this study indicate that the quality of the fundus image is better using the histogram equation before optimization than after optimization. In other words, Cukcoo optimization is not suitable for increasing the performance of the histogram equation in improving fundus image quality

The Cuckoo Optimization Algorithm Enhanced Visualization of Morphological Features of Diabetic Retinopathy

This research compares strategies for identifying diabetic retinopathy (DR) using fundus image and discusses the efficiency of various image pre-processing techniques to enhance the quality of fundus images. Fundus images in medical image processing often suffer from non-uniform lighting, low contrast, and noise issues, which necessitate image pre-processing to enhance their quality. The study evaluates the effectiveness of several optimization techniques in selecting the best technique for identifying DR. One of the image pre-processing techniques compared in the study involves comparing negative images, dark contrast stretch, light contrast stretch, and partial contrast stretch, which are then evaluated using standard performance metrics such as NIQE, PNSR, MSE, and entropy. The results are further optimized using the Cuckoo Search Algorithm. The proposed technique produces better image quality improvements in several performance metrics, such as MSE, NIQE, PSNR, and entropy. Bright Contrast Stretch outperforms other techniques in NIQE Mean 5.2850, Entropy 5.0193, NIQE Standard deviation 0.2261, and Entropy 0.2612.

Lightness Perception of Fabrics Under Non-uniform and Uniform Lighting Conditions

Lightness Perception of Fabrics Under Non-uniform and Uniform Lighting Conditions

Design and Optimization of Photovoltaic System in Full-Chain Ground-Based Validation System of Space Solar Power Station

In the face of the increasing depletion of non-renewable energy sources and increasingly serious environmental problems, the development of green and environmentally friendly renewable energy sources cannot be delayed. Because of the far-reaching development potential of solar energy, solar power has become an important research object for power development. The available solar energy in space is several times greater than that on Earth. Solar energy from space can be collected by a space solar power station (SSPS) and transmitted to the ground by wireless power transfer. In the full-chain ground-based validation system of SSPS-OMEGA, the spherical concentrator is used, and the light intensity distribution on the solar receiver is non-uniform. The non-uniform light intensity makes the output current of each photovoltaic (PV) cell on the solar receiver greatly different, and causes power losses, known as the mismatch problem. This paper proposes a simple, efficient and easy-to-implement method to optimize the structure of PV arrays to reduce the effect of non-uniform light on the output performance of each PV cell, which is beneficial to the topology of PV arrays and also effectively improves the layout rate. Then, a differential power processing (DPP) converter with a simple structure and easy control is designed to further deal with the power mismatch problem between series-connected PV modules. Finally, a simulation circuit model and a physical hardware model of the differential power processing PV system are built and used in the full-chain ground-based validation system of SSPS-OMEGA. The results demonstrate that the influence of non-uniform lighting on PV cells is effectively reduced, the output power of PV modules connected in series under non-uniform light distribution is substantially increased, and the photoelectric conversion efficiency is significantly improved.