Illumination-reflectance Model Research Articles

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

BackgroundCOVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time.ResultsIn this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination–Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient.ConclusionOur proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript’s availability of the data and materials under the declaration section for access.

Method for Correcting Low-illumination Images Based on Adaptive Two-dimensional Gamma Function

A two-dimensional gamma function-based color image correction method based on the illumination-reflection model and multi-scale theory is proposed. Firstly, the original image is histogram equalized, then converted to HSV color space, and the light components of the scene are extracted by multi-scale Gaussian function, and then the parameters of the 2D gamma function are adjusted by the distribution characteristics of the light components to construct a 2D gamma function to enhance the image. Finally, the images are fused. The comparison with the classical algorithm shows that the algorithm in this paper can better reduce the effect of uneven illumination on the image and improve the brightness and contrast of the image.

Weak‐Light Image Enhancement Method Based on Adaptive Local Gamma Transform and Color Compensation

In weak‐light environments, images suffer from low contrast and the loss of details. Traditional image enhancement models are usually failure to avoid the issue of overenhancement. In this paper, a simple and novel correction method is proposed based on an adaptive local gamma transformation and color compensation, which is inspired by the illumination reflection model. Our proposed method converts the source image into YUV color space, and the Y component is estimated with a fast guided filter. The local gamma transform function is used to improve the brightness of the image by adaptively adjusting the parameters. Finally, the dynamic range of the image is optimized by a color compensation mechanism and a linear stretching strategy. By comparing with the state‐of‐the‐art algorithms, it is demonstrated that the proposed method adaptively reduces the influence of uneven illumination to avoid overenhancement and improve the visual effect of low‐light images.

Image splicing detection technique based on Illumination-Reflectance model and LBP

A Copy-create digital image forgery is image tampering that merges two or more areas of images from different sources into one composite image; it is also known as image splicing. Excellent forgeries are so tricky that they are not noticeable to the naked eye and don’t reveal traces of tampering to traditional image tamper detection techniques. To tackle this image splicing detection problem, machines learning-based techniques are used to instantly discriminate between the authentic and forged image. Numerous image forgery detection methods to detect and localize spliced areas in the composite image have been proposed. However, the existing methods with high detection accuracy are computationally expensive since most of them are based on hybrid feature set or rely on the complex deep learning models, which are very expensive to train, run on expensive GPUs, and require a very large amount of data to perform better. In this paper, we propose a simple and computationally efficient image splicing forgery detection that considers a trade-off between performance and the cost to the users. Our method involves the following steps: first, luminance and chrominance are found from the input image; second, illumination is estimated from Luminance using Illumination-Reflectance model; third, Local Binary Patterns normalized histogram for illumination and Chrominance is computed and used as the feature vector for classification using the following machine learning algorithms: Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Naive Bayes. Extensive experiments on the public dataset CASIA v2.0 show that the new algorithm is computationally efficient and effective for image splicing tampering detection.

MR Image Enhancement using Adaptive Weighted Mean Filtering and Homomorphic Filtering

Magnetic resonance image enhancement plays crucial role in numerous bio-medical applications. In this paper, the noisy magnetic resonance (MR) brain images were enhanced using Adaptive Weighted Mean Filtering (AWMF) and homomorphic filtering. The MR images always suffer from low contrast. Homomorphic filtering is popular technique to enhance the image contrast. Homomorphic filtering works based on illumination-reflectance model. It improves the image quality by doing contrast enhancement and dynamic range compression simultaneously. In general, MR images are affected by Rician noise, salt and pepper noise and Gaussian noise. Salt and pepper noise (SPN) considerably reduce the quality of the MR images. Contrast ratio and image quality is significantly degraded in the presence of SPN. Pre-processing is required for noisy MR images before applying to homomorphic filter. Many techniques have been proposed to de-noise the salt and pepper noise such as mean, median and adaptive filters. These filters are used to eliminate low level of SPN. High level of SPN can be eliminated by AWMF. In pre-processing, the AWMF is used to denoising the noisy images. Then de-noised image is enhanced using homomorphic filter. The efficiency of the proposed method is compared with median filter (MF) and based on pixel density filter (BPDF). The simulation results show that our proposed algorithm is more efficient than existing algorithms.

Adaptive image enhancement method for correcting low-illumination images

In this study, to improve the adaptability of image enhancement in images with low illumination, a colored image correction method based on nonlinear functional transformation according to the illumination-reflection model and multiscale theory is proposed. First, the original RGB image is converted to HSV color space, and the V component is used to extract the illumination component of the scene using the multiscale Gaussian function. Then, a correction function is constructed based on the Weber-Fechner law, and two images are obtained through adaptive adjustments to the image enhancement function parameters based on the distribution profiles of the illumination components. Finally, an image fusion strategy is formulated and used to extract the details from the two images. Compared with the classic algorithm, the proposed algorithm can improve the overall brightness and contrast of an image while reducing the impact of uneven illumination. The enhanced images appear clear, bright, and natural.

Improved Method for Enhancing Dark Images based on CLAHE and Morphological Reconstruction

Abstract The image acquired are often flawed due to various defects from environmental scenarios such as indoor lighting, cloudy weather etc especially at night time. The dark image contains compressed dynamic range that can be enhanced for understanding the detailed information. In this investigative work, an improved method for enhancing extremely dark images is proposed utilizing the concept of illumination reflection model. This improved technique is based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and reconstruction done using morphological processing with Top-hat transformation. The image is perceived in HSV color system and V component is estimated. This intensity (V) component is normalized and the inverse of the intensity component is computed. Then CLAHE algorithm is applied on the negative image. Then multiscale image enhancement is applied to the resultant image. Gamma enhancement is used to adaptively adjust the brightness component of an image. After gamma enhancement, the results of two gamma enhanced images with different gamma values are obtained. Principal Component Analysis extracts the significant information from these images that can be used for image fusion. During the PCA based image fusion, the weight value is adaptively calculated by using morphological Top-hat transformation to improve the quality of the image and illuminate the inconsistent background pixels. The proposed method focuses detail enhancement of extremely dark images by preserving the edges and structures. Experimental validations and results show the better performance of the proposed method than the existing method in terms of qualitative and quantitative measures.

Local Centre of Mass Face for face recognition under varying illumination

In this work we propose a novel method to extract illumination insensitive features for face recognition called local centre of mass face (LCMF). In this LCMF approach the gradient angle between the centre of mass and centre pixel of a selected neighborhood is extracted. Theoretically it is shown that this feature is illumination invariant using the Illumination Reflectance Model (IRM) and is robust to different illumination variations. It is also shown that this method does not involve any explicit computation of Luminance (L) component and as centre of mass is an inherent feature of a mass distribution, its slope with the centre pixel of the neighborhood has local edge preserving capabilities. The angle of the slope obtained using Centre of Mass with the centre pixel of the neighborhood is used as a feature vector. This feature vector is directed from the darkest section of the neighborhood to the brightest section of the neighborhood as Centre of Mass is always positioned towards the brighter side of a mass distribution and hence encrypts the edge orientation. Using the L1 norm distance measure, these feature vectors are used to classify the images. The method does not involve any preprocessing and training of images. The proposed method has been successfully tested under different illumination variant databases like AR, CMU-PIE, and extended Yale B using standard protocols, and performance is compared with recently published methods in terms of rank-1 recognition accuracy. The method is also applied on Sketch-Photo pair database like CUHK. For unbiased or fair performance evaluation, the Sensitivity and Specificity are also being measured for the proposed method on all the databases. The proposed method gives better accuracy performance and outperforms other recent face recognition methods.

Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain

We propose a novel approach for low-light image enhancement. Based on illumination-reflection model, the guided image filter is employed to extract the illumination component of the underlying image. Afterwards, we obtain the reflection component and enhance it by nonlinear functions, sigmoid and gamma, respectively. We use the first-order edge-aware constraint in the gradient domain to achieve good edge preserving features of enhanced images and to eliminate halo artefact effectively. Moreover, the resulting images have high contrast and ample details due to the enhanced illumination and reflection component. We evaluate our method by operating on a large amount of low-light images, with comparison with other popular methods. The experimental results show that our approach outperforms the others in terms of visual perception and objective evaluation.

Illumination and Reflectance Estimation with its Application in Foreground Detection.

In this paper, we introduce a novel approach to estimate the illumination and reflectance of an image. The approach is based on illumination-reflectance model and wavelet theory. We use a homomorphic wavelet filter (HWF) and define a wavelet quotient image (WQI) model based on dyadic wavelet transform. The illumination and reflectance components are estimated by using HWF and WQI, respectively. Based on the illumination and reflectance estimation we develop an algorithm to segment sows in grayscale video recordings which are captured in complex farrowing pens. Experimental results demonstrate that the algorithm can be applied to detect the domestic animals in complex environments such as light changes, motionless foreground objects and dynamic background.

True Color Image Enhanced by Wavelet Illumination-Reflection Model

When a true color image is enhanced, the same hue should be kept and the useful details should be strengthened in the image enhanced. According to the imaging principle, wavelet illumination-reflection method is proposed. Firstly, true color image is transformed from RGB space to HSV space. Secondly, the saturation is attenuated by CLAHE. Thirdly, the value is decomposed into illumination and reflection by wavelet illumination-reflection model. The details of the reflection are strengthened. The dynamic range of the illumination is reduced in order to enhance the image. Experiments and analysis show that the enhancement algorithm based on wavelet illumination-reflection model is obviously better than multi-scales Retinexs with color restoration (MSRCR).

Digital Medical Image Enhanced by wavelet Illumination-Reflection Model

When a digital medical image is enhanced, the useful details of the image should be strengthened， but the details can not be strengthened by these algorithms based on traditional illumination-reflection model. According to the imaging principle and medical requirement, wavelet illumination-reflection model and a new algorithm based on the model are proposed. The image is decomposed into illumination and reflection by wavelet illumination-reflection model. The details of the reflection are strengthened. The dynamic range of the illumination is reduced in order to enhance the image. Experiments and analysis show that the method is obviously better than Histogram Equalization, Homomorphic Filtering and multi-scales Retinex. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1866

A Research on the Location for Illumination Component of Poorly Illuminated Face Images in Fourier Spectrum

Although a face image can be described in illumination-reflectance model, the illumination and reflectance components are not distinctly separated in the frequency domain after the Fourier transformation. In fact, the illumination components of a poorly illuminated face image can be divided into two regions, namely low frequency region and vertical center region. This paper proposed two methods to identify the locations of illumination component in the face images which consist of different subjects and illumination directions. By using inspection method and mean square error (MSE) function, the illumination locations can be determined accurately. The largest location of illumination component discovered via the research will be used as reference parameters to design a customized filter before it is implemented in homomorphic filtering technique.

Digital Medical Image Enhanced by Stationary Wavelet Transform

Abstract: When a digital medical image is enhanced by Histogram Equalization, Homomorphic Filtering or multi-scales Retinex, the useful details of the image can not be strengthened. According to the imaging principle and medical requirement, a new method is proposed. The image is decomposed into illumination and reflection by wavelet illumination-reflection model. The details of the reflection are strengthened by soft-threshold method. The dynamic range of the illumination is reduced by Gaussian filtering in order to enhance the image. Experiments and analysis show that the method is obviously better than these algorithms.

Detection of navel surface defects based on illumination-reflectance model

Detection of navel surface defects based on illumination-reflectance model

Illumination invariant feature extraction and mutual-information-based local matching for face recognition under illumination variation and occlusion

An efficient method for face recognition which is robust under illumination variations is proposed. The proposed method achieves the illumination invariants based on the illumination-reflection model employing local matching for best classification. Different filters have been tested to achieve the reflectance part of the image, which is illumination invariant, and maximum filter is suggested as the best method for this purpose. A set of adaptively weighted classifiers vote on different sub-images of each input image and a decision is made based on their votes. Image entropy and mutual information are used as weight factors. The proposed method does not need any prior information about the face shape or illumination and can be applied on each image separately. Unlike most available methods, our method does not need multiple images in training stage to get the illumination invariants. Support vector machines and k-nearest neighbors methods are used as classifier. Several experiments are performed on Yale B, Extended Yale B and CMU-PIE databases. Recognition results show that the proposed method is suitable for efficient face recognition under illumination variations.

Wavelet local binary patterns fusion as illuminated facial image preprocessing for face verification

Poor illumination condition is recognized as one of the major problem in contemporary two-dimensional (2D) face verification system. It causes large variation in facial images and degrades the performance of the system. Many works of resolving illumination variation in face verification have been reported in the past decades. In this paper, a facial image illumination invariant technique is devised based on the fusion of wavelet analysis and local binary patterns. Particularly, illumination–reflectance model is used to detach illumination and reflectance components with multi-resolution nature of wavelet analysis. The illumination component that resides in low spatial-frequency wavelet subband is first rid off efficiently. The reflectance components that reside in high and middle spatial-frequency wavelet subbands are enhanced with local binary patterns histogram. Finally, two processed images are fused through wavelet image fusion. This technique works out promisingly in achieving better recognition results on YaleB, CMU PIE and FRGC face databases in comparison with existing illumination invariant techniques.

Wavelet-based illumination invariant preprocessing in face recognition

Performance of a contemporary two-dimensional face-recognition system has not been satisfied due to the variation in lighting. As a result, many works of solving illumination variation in face recognition have been carried out in past decades. Among them, the Illumination-Reflectance model is one of the generic models that is used to separate the individual reflectance and illumination components of an object. The illumination component can be removed by means of image-processing techniques to regain an intrinsic face feature, which is depicted by the reflectance component. We present a wavelet-based illumination invariant algorithm as a preprocessing technique for face recognition. On the basis of the multiresolution nature of wavelet analysis, we decompose both illumination and reflectance components from a face image in a systematic way. The illumination component wherein resides in the low-spatial-frequency subband can be eliminated efficiently. This technique works out very advantageously for achieving higher recognition performance on YaleB, CMU PIE, and FRGC face databases.

Design of a systolic-pipelined architecture for real-time enhancement of color video stream based on an illuminance–reflectance model

A high performance digital architecture for the implementation of a nonlinear image enhancement technique is proposed in this paper. The image enhancement is based on an illuminance–reflectance model which improves the visual quality of digital images and video captured under insufficient or non-uniform lighting conditions. The algorithm shows robust performance with appropriate dynamic range compression, good contrast, accurate and consistent color rendition. The algorithm contains a large number of complex computations and thus it requires specialized hardware implementation for real-time applications. Systolic, pipelined and parallel design techniques are utilized effectively in the proposed FPGA-based architectural design to achieve real-time performance. Approximation techniques are used in the hardware algorithmic design to achieve high throughput. The video enhancement system is implemented using Xilinx's multimedia development board that contains a VirtexII-X2000 FPGA and it is capable of processing approximately 63 Mega-pixels (Mpixels) per second.

Butterworth equations for homomorphic filtering of images

In digital image processing, the homomorphic filtering approach is derived from an illumination–reflectance model of the image. Homomorphic filtering can perform simultaneous dynamic range compression and contrast enhancement. Crucial for the success of the homomorphic approach is the selection of an appropriate frequency-domain filter function in order to modify the illumination and reflectance components of an image differently. The author found Butterworth type highpass equations far superior to other frequency-domain filter functions, including Gaussian equations, making the Butterworth highpass suitable for use with the homomorphic filtering approach. The program was written in Microsoft (MS) Visual C++™ (filter) as well as MS Visual Basic™ (user interface) to run as a module under the image processing software package Image-Pro Plus™.