Illumination Correction Method Research Articles

The impact of variable illumination on vegetation indices and evaluation of illumination correction methods on chlorophyll content estimation using UAV imagery

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.

Cross-Track Illumination Correction for Hyperspectral Pushbroom Sensor Images Using Low-Rank and Sparse Representations

A hyperspectral pushbroom sensor scans objects line-by-line using a detector array, and a cross-track illumination error (CTIE) exists in the imagery acquired in this way. When the illumination of the individual cells of the detector is not aligned well, or if some of the cells are degraded or old, the acquired images will exhibit nonuniform illumination in the cross-track direction. As additive Gaussian noise is found widely in hyperspectral images (HSIs), we develop a unified mathematical model that describes the image formation process corrupted by the CTIE and additive Gaussian noise. The CTIE produced by line-by-line scanning is replicated and modeled as an offset term with the equivalent values in the direction of flight. The main contribution of this study is the development of a hyperspectral image cross-track illumination correction (HyCIC) method, which corrects the cross-track illumination using column (along-track) mean compensation with total variation and sparsity regularizations, and attenuates the Gaussian noise by using a form of low-rank constraint. The effectiveness of the proposed method is illustrated using semireal data and real HSIs. The performance of the proposed HyCIC is found to be better than other existing methods.

Quantitative analysis of illumination and detection corrections in adaptive light sheet fluorescence microscopy.

Light-sheet fluorescence microscopy (LSFM) is a high-speed, high-resolution and minimally phototoxic technique for 3D imaging of in vivo and in vitro specimens. LSFM exhibits optical sectioning and when combined with tissue clearing techniques, it facilitates imaging of centimeter scale specimens with micrometer resolution. Although LSFM is ubiquitous, it still faces two main challenges that effect image quality especially when imaging large volumes with high-resolution. First, the light-sheet illumination plane and detection lens focal plane need to be coplanar, however sample-induced aberrations can violate this requirement and degrade image quality. Second, introduction of sample-induced optical aberrations in the detection path. These challenges intensify when imaging whole organisms or structurally complex specimens like cochleae and bones that exhibit many transitions from soft to hard tissue or when imaging deep (> 2 mm). To resolve these challenges, various illumination and aberration correction methods have been developed, yet no adaptive correction in both the illumination and the detection path have been applied to improve LSFM imaging. Here, we bridge this gap, by implementing the two correction techniques on a custom built adaptive LSFM. The illumination beam angular properties are controlled by two galvanometer scanners, while a deformable mirror is positioned in the detection path to correct for aberrations. By imaging whole porcine cochlea, we compare and contrast these correction methods and their influence on the image quality. This knowledge will greatly contribute to the field of adaptive LSFM, and imaging of large volumes of tissue cleared specimens.

Enhancement Method for Color Retinal Fundus Images Based on Structural Details and Illumination Improvements

Retinal images show an essential role in Ophthalmology to diagnosis wide set of diseases. In this direction, using retinal images in computerized techniques increases the ability of diagnosis in fast time effectively. However, some eye diseases and capturing conditions produce low-quality retinal images, which reduces the diagnosis ability for machines and humans. To solve that, several works have been proposed to enhance retinal images. But they show a lot of negative observations, especially with color images of retina. In this paper, a novel enhancement algorithm for color retinal images is proposed. It consists of three stages; firstly, the appearance of visual details is increased by enhancing the contrast of structural details of retinal image using details enhanced and Bilateral filters. Then, a novel uneven illumination correction method is proposed to solve the uneven illumination problem adaptively. Finally, the advantages of both previous stages are combined using HSV color model to produce the final enhanced retinal images. DRIVE and STARE benchmark datasets are used to conduct experiments. The results were compared with histogram Equalized (HE), Contrast Stretching (CS), the adaptive histogram equalization (CLAHE) and Zhou’s method retinal enhancement methods. In conclusion, the results show that the proposed method shows high performance compared with the corresponding enhancement methods.

A simple deep learning based image illumination correction method for paintings

Abstract Image illumination correction has been a long standing topic for research in the Computer Vision problem. However, all previous literature on this topic has either been statistical in nature in the sense that a specified algorithm has been developed for approaching a particular case of illumination normalization, or involves extremely complex deep learning methods for illumination correction of either one of over illuminated or under illuminated images. We present here a very simple deep learning based image illumination correction architecture which works on color images of paintings irrespective of whether they are under or over illuminated. We have tested the results using a synthetic database as well as on real world painting images of diverse nature.

Assessing the spatial, spectral, and temporal consistency of topographically corrected Landsat time series composites across the mountainous forests of Nepal

In mountainous regions slope and aspect result in variations in the illumination condition and the same land cover type can therefore show differences in the reflectance depending on the orientation of the terrain slope towards the sun and the sensor of the satellite. Different topographic illumination correction methods exist and their performance varies depending on e.g. sun zenith and land cover type. Similarly a variety of evaluation criteria exist to assess the performance of the topographic correction methods and each evaluation criteria usually only considers certain aspects of the ability of the topographic correction method in reducing signal-to-noise. In this article we present a novel framework for the evaluation of topographically corrected image composites using cloud computing. We evaluated six topographic correction methods (Bin Tan, C-Correction, Minnaert with slope, Sun-Canopy-Sensor plus C-Correction, Statistical-Empirical, and Variable Empirical Coefficient Algorithm) in forest areas of four Landsat footprints in Nepal for a time series of image composites from 1988 to 2016. Our evaluation shows that the Statistical-Empirical topographic correction method provides the best overall performance, but in some years and footprints also other methods can show the best performance. We discuss these differences and related reasons in detail and give recommendations on the best use and evaluation of topographic correction methods.

An automated glaucoma screening system using cup-to-disc ratio via Simple Linear Iterative Clustering superpixel approach

Glaucoma is an ocular disease caused by damaged optic nerve head (ONH) due to high intraocular pressure (IOP) within the eyeball. Usually, glaucoma patients will not realize the presence of this disease due to lack of visible early symptoms such as pain and redness mark. The disease can cause permanent blindness if it is not treated immediately. Hence, glaucoma screening is very crucial in detecting the disease during the early stages. There are various types of glaucoma screening tests such as tonometry test which is based on IOP measurement, ophthalmology test which is based on shape and color of the eyes, and pachymetry test which is based on complete field vision measurement. All these three screening tests involve manual assessment which is time-consuming and costly. Therefore, an efficient glaucoma screening system that can automatically analyze the severity level of the disease is very much needed. Thus, the main objective of this paper is to develop an automatic glaucoma screening system based on superpixel classification by providing a high-quality input image. Firstly, input images are undergone preprocessing methods to cater for noise removal and illumination correction. This is emphasized in the implementation of the anisotropic diffusion filter and illumination correction method. The pixels of the input images are then aggregate into superpixels using Simple Linear Iterative Clustering (SLIC) approach. Then, image features based on histogram data and textural information are extracted on each superpixel using statistical pixel-level (SPL) method. The prominent features are then fed into Support Vector Machine (SVM) classifier to classify each superpixel into optic disc, optic cup, blood vessel, and background regions. The classifier is also used to determine the boundaries of both optic disc and optic cup. Lastly, the segmented optic disc and optic cup are used to determine the presence of glaucoma using cup-to-disc ratio (CDR) measurement. The proposed method has been tested on RIM-One database. The experimental results have successfully distinguished optic disc and optic cup from the background with an average accuracy and sensitivity of 98.6% and 92.3%, respectively tested on linear kernel.

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.

Adaptive uneven illumination correction method of document images

Under condition of uneven illumination, directly recognizing the document image by OCR would lead to a bad effect, which is a difficult point in document recognition. To solve this problem above, a Retinex-based illumination correction method was proposed. Firstly, the images were supposed to be di vided into blocks, which were processed respectively by Otsu binarization. The light conditions can be got through the feedback results of processing, which helps to have uneven illumination image blocks achieved. Then images can be enhanced by the improved Retinex method, which extends dynamic range of image block gray value and enhances details of image block. Finally, images can be corrected by sauvola binarization processing. Experimental results show that the algorithm can eliminate the impact of uneven illumination. Compared with the common local binarization and enhancement methods, the OCR recognition rates are significantly increased. At the same time, the combination of global and local methods promotes the applicable scope of being in condition of samples and different illumination.

Validation of non-uniform illumination correction techniques in microscopic digital TB images using image sharpness measures

Background: Tuberculosis (TB) is a communicable disease for which an early diagnosis is essential to control the disease. The microscopy-based TB screening is the conventional method employed for TB identification in sputum smears. Fluorescence microscopy-based diagnosis provides improved sensitivity and benefits large number of TB burdened communities across the globe. Microscopic images are often corrupted by intensity variations because of inherent imperfections of the image formation process. This may result in false positives which is the potential shortcoming of fluorescence microscopy. Methods & Materials: The fluorescence-stained slides were prepared at South African National Health Laboratory Services, Groote Schuur Hospital in Cape Town. The images (N=100) were captured using a camera in monochrome binning mode attached to a 20x objective fluorescence microscope of 0.5 numerical aperture. The camera (AxioCam HR) has a resolution of 4164 x 3120 with a pixel size 6.45 μm (h) x 6.45 μm (v). The illumination correction methods adopted in this work include surface fitting method, multiple regression method and bidirectional empirical mode decomposition. The results of illumination correction are validated using the image sharpness measures. This includes derivative-based, statistical, histogram-based and transform-based parameters. Results: It is observed from the results that surface polynomial fit-based correction performs better among the illumination correction techniques. The intensity profile of the corrected image reveals the performance of the method. Also, the most significant sharpness parameters-based on derivative, statistical, image histogram and transform showed the effectiveness of the method which could be suitable for the non-uniform illumination correction of these images. Results demonstrate that the seven most significant (p < 0.0001) sharpness parameters present distinct variation with the implementation of surface fitting illumination correction method. This proves the suitability and efficiency of the method for these images. Conclusion: Although illumination correction is a long standing research topic and many algorithms have been proposed, the selection of the optimal algorithm for specific experimental microscopy applications remains ad hoc. The application of the surface fitting method is especially useful in pre-processing of digital sputum smear images, and could be used for reliable identification and classification of TB objects (bacilli and non-bacilli).

Pipeline for illumination correction of images for high-throughput microscopy.

The presence of systematic noise in images in high-throughput microscopy experiments can significantly impact the accuracy of downstream results. Among the most common sources of systematic noise is non-homogeneous illumination across the image field. This often adds an unacceptable level of noise, obscures true quantitative differences and precludes biological experiments that rely on accurate fluorescence intensity measurements.In this paper, we seek to quantify the improvement in the quality of high-content screen readouts due to software-based illumination correction. We present a straightforward illumination correction pipeline that has been used by our group across many experiments. We test the pipeline on real-world high-throughput image sets and evaluate the performance of the pipeline at two levels: (a) Z′-factor to evaluate the effect of the image correction on a univariate readout, representative of a typical high-content screen, and (b) classification accuracy on phenotypic signatures derived from the images, representative of an experiment involving more complex data mining. We find that applying the proposed post-hoc correction method improves performance in both experiments, even when illumination correction has already been applied using software associated with the instrument.To facilitate the ready application and future development of illumination correction methods, we have made our complete test data sets as well as open-source image analysis pipelines publicly available. This software-based solution has the potential to improve outcomes for a wide-variety of image-based HTS experiments.

Statistics Properties of Asphalt Pavement Images for Cracks Detection

In order to overcome the illumination interference on the asphalt pavement cracks detection, the higherorder statistics based non-uniformity illumination correction method is firstly proposed, which improves the image quality significantly. The local regions of pavement have similar geometric texture, and then the probability distribution of image pixel values in local regions is also similar. Based on the fact a new method to calculate statistical correlation characteristics of pixel values in local region is adopted. And then the feature vector is built by joint the pixel value and the characteristics values of statistical correlation. Support vector machine is used for rough detection by training the sample feature vectors and prediction processing. Finally morphological dilation, corrosion, connectivity region label and area constraints are adopted to remove impurities and complete the finer cracks detection. The experimental results show that compared with the detection method based on Otsu image segmentation, the detection accuracy is significantly improved, the proposed method can not only detect the cracks with weak contrast but also detect the tiny cracks.

블록 기반 밝기 표준화를 통한 이진영상의 고속 불균일 조명 보정

We investigated a novel fast non-uniform illumination correction method for bi-level images. The proposed method divides a bi-level image into sub-images and roughly estimates block-wise illumination by low pass filtered maximum values of sub-images. After that, we apply bilinear interpolation using the block-wise illumination to estimate non-uniform illumination, and compensate for the effect of non-uniform illumination using the estimated illumination. Since the proposed method is not based on computation intensive iterative optimization, the proposed method can be used effectively for applications that require fast correction of non-uniform illumination. In simulations, the proposed method showed more than 20 times faster speed than existing entropy minimization method. Moreover, in simulations and experiments, the restored images by the proposed method were more close to true images than images restored by conventional method.

Virtual illumination grid for correction of uncontrolled illumination in facial images

Face recognition under uncontrolled illumination conditions is still considered an unsolved problem. In order to correct for these illumination conditions, we propose a virtual illumination grid (VIG) approach to model the unknown illumination conditions. Furthermore, we use coupled subspace models of both the facial surface and albedo to estimate the face shape. In order to obtain a representation of the face under frontal illumination, we relight the estimated face shape. We show that the frontal illuminated facial images achieve better performance in face recognition. We have performed the challenging Experiment 4 of the FRGCv2 database, which compares uncontrolled probe images to controlled gallery images. Our illumination correction method results in considerably better recognition rates for a number of well-known face recognition methods. By fusing our global illumination correction method with a local illumination correction method, further improvements are achieved.