Illumination Correction Algorithm Research Articles

Non-uniform illumination correction algorithm for cytoendoscopy images based on illumination model

Non-uniform illumination correction algorithm for cytoendoscopy images based on illumination model

Adapting to the Dark: A Novel Adaptive Low Light Illumination Correction Algorithm for Video Sequences in Wireless Communications

The lower image quality and higher noise levels of low light images can have a substantial effect on wireless communication. Images typically have less brightness, less clarity, and more picture noise in low light situations. The precision and dependability of picture-based communication may be impacted by this decline in image quality. Low light levels can be detrimental to video chats since it becomes difficult for the camera to get clear pictures of the user's face. This may lead to decreased video quality, trouble with facial recognition, and a generally worse user experience when communicating over video. Low light illumination correction algorithms are frequently made to work quickly and effectively in order to enable real-time video-based wireless communication. Correcting uneven illumination or low light in images is crucial for various fields; as such images pose challenges for human recognition, computer vision algorithms, and multimedia algorithms. An adaptive illumination correction algorithm based on a simple log transformation approach enhanced by a hyperbolic beta transformation and a logarithmic image processing is presented in this research. It has an adaptive optimization function that adjusts itself based on the image’s illumination and reflectance. Low computation and minimal parameter optimization are the hallmarks of this method. A variety of illuminated images and video sequences from various datasets are used to evaluate the suggested adaptive illumination approach. This adaptive illumination approach's experimental findings are impressive compared to those produced using prior techniques. The results reveal that the proposed system can adapt to various image-based communication and surveillance video sequences taken under various illumination conditions.

ADAPTIVE ILLUMINATION CORRECTION ALGORITHM BASED ON RETINEX TECHNOLOGY FOR CAMERA TRAP IMAGES

Abstract. Camera traps generating a huge number of images help to study and monitor the wildlife. However, camera traps work at any time of the day and under any weather conditions. Therefore, many images have low or high illumination, blurring, and other defects. This complicates image analysis by both humans and computer systems. In this study, we develop an adaptive illumination correction algorithm based on a modified Multi-Scale Retinex (MSR). First, we accelerate computation by using recursive implementation of the Gaussian filter and utilizing look-up tables to find logarithms and new brightness values. Second, response of the MSR function is transformed by a modified threshold normalization to improve image quality. The upper and lower thresholds are calculated based on statistical information. Finally, we offer automatic adjustment of parameters depending on the area of the image in order to increase usability. Proposed algorithm was tested with various settings on a set of images obtained from camera traps. Experimental results show a high potential for its application.

Detection method based on a coupled illumination correction algorithm for the detection of surface defects in ZrO2 ceramic bearing balls.

To address the problem of feature texture loss in defective images of zirconia bearing balls on unbalanced illumination backgrounds, a coupled algorithm of the local gamma function transform-block interpolation balancing equation is designed. A combination of a light reflection imaging model and a three-dimensional Gaussian surround function is used to extract the illumination estimate of the original image. Subsequent illumination balance correction is performed in the value color space. The contrast between the edges of the feature texture and the background is analyzed to construct a locally adaptive gamma function transformation model. We establish local block interpolation balance equations to eliminate the effects caused by illumination that is too high or too low. The experimental results show that the information entropy of the images after illumination correction changes in the range of 5% or less. The standard deviation and mean gradient can be increased by 15.21% and 14.78%, respectively.

Illumination correction of dyed fabric based on extreme learning machine with improved ant lion optimizer

AbstractIn order to eliminate the influence of scene illumination on the evaluation of the color difference of dyed fabrics, this paper proposes a dyed fabric illumination correction algorithm based on the extreme learning machine (ELM) with grey wolf optimizer (GWO)‐optimized ant lion optimizer (ALO). Firstly, the Grey Edge framework is used to extract the features of the dyed fabric image as the input vector. Then, to improve the optimization ability of the ALO algorithm, the GWO algorithm is used to provide a set of optimized initial populations to the ALO algorithm, and then the improved ALO algorithm is used to optimize the parameters of the ELM. Finally, the proposed GWO‐ALO‐ELM algorithm is used to correct the illumination of the dyed fabric, and restore the graphics to the effect display under standard illumination through the diagonal reduction model. Compared with the experimental results of GWO‐ELM, ALO‐ELM, backpropagation (BP), ELM, random vector function link (RVFL), and other algorithms, it can be seen that the GWO‐ALO‐ELM algorithm proposed in this paper has good predictive value and quasi‐bias effect, and good stability.

Research on Subway Pedestrian Detection Algorithm Based on Big Data Cleaning Technology

The pedestrian detection model has a high requirement on the quality of the dataset. Concerning this problem, this paper uses data cleaning technology to improve the quality of the dataset, so as to improve the performance of the pedestrian detection model. The dataset used in this paper is obtained from subway stations in Beijing and Nanjing. The data images’ quality is subject to motion blur, uneven illumination, and other noisy factors. Therefore, data cleaning is very important for this paper. The data cleaning process in this paper is divided into two parts: detection and correction. First, the whole dataset goes through blur detection, and the severely blurred images are filtered as the difficult samples. Then, the image is sent to DeblurGAN for deblur processing. 2D gamma function adaptive illumination correction algorithm is used to correct the subway pedestrian image. Then, the processed data is sent to the pedestrian detection model. Under different data cleaning datasets, through the analysis of the detection results, it is proved that the data cleaning process significantly improves the detection model’s performance.

Data Augmentation with Illumination Correction in Sematic Segmentation

In the training process of the deep learning network, data augmentation can effectively improve accuracy. Presently, the commonly used data augmentation methods have been mainly divided into space transformation and color transformation. However, these methods do not take into account the effects of the image’s illumination factor. Under natural conditions, the color, angle, and strength of the light will cause the color of the object to change, which is likely to cause segmentation errors. In this article, we start from the angle of illumination and perform data augmentation on the image. By applying the illumination correction algorithm based on Retinex theory, we are able to enhance the data and achieved good results in the experiment. However, there still remains some problems within the current algorithms based on Retinex theory. Especially for images with uniform illumination conditions, which can be overexposed after processed by these algorithms. In response to this problem, we have improved the relevant algorithm. The improved algorithm further improves the accuracy of semantic segmentation on the original basis.

Non-uniform illumination correction based on multi-scale Retinex in digital image correlation.

Digital image correlation (DIC) is an effective optical measurement method. It aims to obtain the displacement field and strain field of the measured object by correlating two digital speckle images before and after deformation. In the actual acquisition of speckle images, due to the large volume of the measured object, the light source cannot cover all areas evenly or has some random change. These issues may easily lead to a non-uniform distribution of light intensity speckle images and reduce the quality of speckle images, which affects the accuracy of DIC measurement to a certain extent. To solve this problem, a non-uniform illumination correction algorithm based on multi-scale Retinex is introduced. First, to analyze the influence of non-uniform illumination on DIC measurement accuracy, the displacement comparison experiment of the numerical simulation speckle images with different non-uniform illumination is conducted. Then, a non-uniform illumination correction algorithm based on multi-scale Retinex is applied to reduce or eliminate the effects of non-uniform illumination by the simulation experiment. Finally, the quantitative measurement of rigid body rotation and uniaxial tensile experiment in plane is studied to verify the feasibility of the correction method for the speckle images. The experimental results show that the measurement accuracy of DIC is improved significantly with the aid of non-uniform illumination variation correction.

Underwater image enhancement framework and its application on an autonomous underwater vehicle platform

Underwater imaging has been increasingly employed in vision-based marine research. However, the inappropriate installation of a light source and the complex underwater environment will result in the uneven illumination and overexposure on the captured images. To address these issues, an underwater image enhancement framework for autonomous underwater vehicles platform is proposed, which consists of underwater light source optimization and illumination nonuniformity correction. The light source optimization method improves the imaging quality by computing an appropriate angle of the light casting. In this way, the center of the field of view is always well lit. In addition, an adaptive filter-based illumination correction algorithm is proposed to solve the uneven illumination caused by the artificial light source. During this process, image block segmentation and the measure of image enhancement index are applied to improve the adaptability and reduce the calculation errors of the filter parameters. A dataset with real underwater images collected under different natural conditions has been built and tested. The experimental results indicate that the proposed method is more adaptive and effective than the typical methods.

NUICNet: Non-Uniform Illumination Correction for Underwater Image Using Fully Convolutional Network

Absorption and scattering in aqueous media would attenuate light and make imaging difficult. Therefore, an artificial light source is usually utilized to assist imaging in the deep ocean. However, the artificial light source typically alters the light conditions to a large extent, resulting in the non-uniform illumination of images. To solve this problem, we propose a non-uniform illumination correction algorithm based on a fully convolutional network for underwater images. The proposed algorithm model the original image as the addition of the ideal image and a non-uniform light layer. We replace the traditional pooling layer with dilated convolution to expand the receptive field and achieve higher accuracy in non-uniform illumination recognition. To improve the perception ability of the network effectively, the original image and parameters which pre-trained on the ImageNet are concentrated. The concentrated information is used as input to the network. Due to the color shift and blurred details of the underwater image, we design the novel loss function, which includes three parts of feature loss, smooth loss, and adversarial loss. Moreover, we built a dataset of the underwater image with non-uniform illumination. Experiments show that our method performs better in subjective assessment and objective assessment than some traditional methods.

A novel hybrid model using the rotation forest-based differential evolution online sequential extreme learning machine for illumination correction of dyed fabrics

This study proposes an ensemble differential evolution online sequential extreme learning machine (DE-OSELM) for textile image illumination correction based on the rotation forest framework. The DE-OSELM solves the inaccuracy and long training time problems associated with traditional illumination correction algorithms. First, the Grey–Edge framework is used to extract the low-dimensional and efficient image features as online sequential extreme learning machine (OSELM) input vectors to improve the training and learning speed of the OSELM. Since the input weight and hidden-layer bias of OSELMs are randomly obtained, the OSELM algorithm has poor prediction accuracy and low robustness. To overcome this shortcoming, a differential evolution algorithm that has the advantages of good global search ability and robustness is used to optimize the input weight and hidden-layer bias of the DE-OSELM. To further improve the generalization ability and robustness of the illumination correction model, the rotation forest algorithm is used as the ensemble framework, and the DE-OSELM is used as the base learner to replace the regression tree algorithm in the original rotation forest algorithm. Then, the obtained multiple different DE-OSELM learners are aggregated to establish the prediction model. The experimental results show that compared with the textile color correction algorithm based on the support vector regression and extreme learning machine algorithms, the ensemble illumination correction method achieves high prediction accuracy, strong robustness, and good generalization ability.

An Uneven Illumination Correction Algorithm for Optical Remote Sensing Images Covered with Thin Clouds

The uneven illumination phenomenon caused by thin clouds will reduce the quality of remote sensing images, and bring adverse effects to the image interpretation. To remove the effect of thin clouds on images, an uneven illumination correction can be applied. In this paper, an effective uneven illumination correction algorithm is proposed to remove the effect of thin clouds and to restore the ground information of the optical remote sensing image. The imaging model of remote sensing images covered by thin clouds is analyzed. Due to the transmission attenuation, reflection, and scattering, the thin cloud cover usually increases region brightness and reduces saturation and contrast of the image. As a result, a wavelet domain enhancement is performed for the image in Hue-Saturation-Value (HSV) color space. We use images with thin clouds in Wuhan area captured by QuickBird and ZiYuan-3 (ZY-3) satellites for experiments. Three traditional uneven illumination correction algorithms, i.e., multi-scale Retinex (MSR) algorithm, homomorphic filtering (HF)-based algorithm, and wavelet transform-based MASK (WT-MASK) algorithm are performed for comparison. Five indicators, i.e., mean value, standard deviation, information entropy, average gradient, and hue deviation index (HDI) are used to analyze the effect of the algorithms. The experimental results show that the proposed algorithm can effectively eliminate the influences of thin clouds and restore the real color of ground objects under thin clouds.

Skin image illumination modeling and chromophore identification for melanoma diagnosis

The presence of illumination variation in dermatological images has a negative impact on the automatic detection and analysis of cutaneous lesions. This paper proposes a new illumination modeling and chromophore identification method to correct lighting variation in skin lesion images, as well as to extract melanin and hemoglobin concentrations of human skin, based on an adaptive bilateral decomposition and a weighted polynomial curve fitting, with the knowledge of a multi-layered skin model. Different from state-of-the-art approaches based on the Lambert law, the proposed method, considering both specular reflection and diffuse reflection of the skin, enables us to address highlight and strong shading effects usually existing in skin color images captured in an uncontrolled environment. The derived melanin and hemoglobin indices, directly relating to the pathological tissue conditions, tend to be less influenced by external imaging factors and are more efficient in describing pigmentation distributions. Experiments show that the proposed method gave better visual results and superior lesion segmentation, when compared to two other illumination correction algorithms, both designed specifically for dermatological images. For computer-aided diagnosis of melanoma, sensitivity achieves 85.52% when using our chromophore descriptors, which is 8~20% higher than those derived from other color descriptors. This demonstrates the benefit of the proposed method for automatic skin disease analysis.

FSAM: A fast self-adaptive method for correcting non-uniform illumination for 3D reconstruction

Since three-dimensional surface texture can display texture information of an object better than its two-dimensional counterpart and can vary with scene illumination and the view angles, it is widely used in virtual reality, computer games and animation applications. Photometric Stereo, as one of the most effective technologies for capturing three-dimensional surface information, has attracted wide attention both from academic researchers and industrial fields. Uniform illumination is the essential condition for capturing and reconstructing surface heightmaps. In practice, non-uniform illumination leads to distorted surface heightmaps, such as distortion and aberration during the capture and reconstruction processes. This paper proposes and assesses a fast and self-adaptive method based on inverse-square law to correct non-uniform illumination for reconstruction of 3D surface heightmaps, and to eliminate the distortions. In order to objectively assess the performance of the illumination correction algorithm, average absolute gradient (AAG) is proposed to compare the surface heightmaps reconstructed using corrected illumination with those reconstructed without illumination correction. Experimental results show that the method is efficient and can produce convincing results.

Improved forest change detection with terrain illumination corrected Landsat images

An illumination correction algorithm has been developed to improve the accuracy of forest change detection from Landsat-derived reflectance data. This algorithm is based on an empirical rotation model and was tested on Landsat image pairs over the Cherokee National Forest, Tennessee; Uinta–Wasatch–Cache National Forest, Utah; San Juan National Forest, Colorado; and Sinkyone Wilderness State Park, California. The illumination correction process successfully eliminated correlation between Landsat reflectance and illumination condition. Comparison to forest-change maps derived from uncorrected images showed significant disagreement, ranging from 23% to 45%. Validated against high-resolution (1m or less) time-serial images, the illumination correction decreased overestimation of forest gains and losses and improved specificity in detection of major forest changes. The overall accuracy increases 34% at the Cherokee Forest site and about 10% at the other three sites. The disagreement rate between change maps from the original and corrected Landsat images increased with increasing terrain inclination angle, with the relationship between illumination condition and the disagreement rate following a V-shaped curve that varied among sites. The lowest disagreement rate occurred when illumination condition was slightly smaller than that of a horizontal field. The correction for topographic illumination should be considered as a standard pre-processing step for land cover classification and land use change detection, especially for mountainous areas.

MSIM: Multistage Illumination Modeling of Dermatological Photographs for Illumination-Corrected Skin Lesion Analysis

Melanoma is the most deadly form of skin cancer and it is costly for dermatologists to screen every patient for melanoma. There is a need for a system to assess the risk of melanoma based on dermatological photographs of a skin lesion. However, the presence of illumination variation in the photographs can have a negative impact on lesion segmentation and classification performance. A novel multistage illumination modeling algorithm is proposed to correct the underlying illumination variation in skin lesion photographs. The first stage is to compute an initial estimate of the illumination map of the photograph using a Monte Carlo nonparametric modeling strategy. The second stage is to obtain a final estimate of the illumination map via a parametric modeling strategy, where the initial nonparametric estimate is used as a prior. Finally, the corrected photograph is obtained using the final illumination map estimate. The proposed algorithm shows better visual, segmentation, and classification results when compared to three other illumination correction algorithms, one of which is designed specifically for lesion analysis.

A new image calibration system in digital colposcopy

Colposcopy is a primary diagnostic method used to detect cancer and precancerous lesions of the uterine cervix. During the examination, the metaplastic and abnormal tissues exhibit different degrees of whiteness (acetowhitening effect) after applying a 3%-5% acetic acid solution. Colposcopists evaluate the color and density of the acetowhite tissue to assess the severity of lesions for the purpose of diagnosis, telemedicine, and annotation. However, the color and illumination of the colposcopic images vary with the light sources, the instruments and camera settings, as well as the clinical environments. This makes assessment of the color information very challenging even for an expert. In terms of developing a Computer-Aided Diagnosis (CAD) system for colposcopy, these variations affect the performance of the feature extraction algorithm for the acetowhite color. Non-uniform illumination from the light source is also an obstacle for detecting acetowhite regions, lesion margins, and anatomic features. There fore, in digital colposcopy, it is critical to map the color appearance of the images taken with different colposcopes into one standard color space with normalized illumination. This paper presents a novel image calibration technique for colposcopic images. First, a specially designed calibration unit is mounted on the colposcope to acquire daily calibration data prior to performing subject examinations. The calibration routine is fast, automated, accurate and reliable. We then use our illumination correction algorithm and a color calibration algorithm to calibrate the exam data. In this paper we describe these techniques and demonstrate their applications in clinical studies.