Retinex Methods Research Articles

Improving the Quality of Digital Images on Identity Cards Using Contrast Stretching and Retinex Methods

Enhancing the image quality of the Indonesian Identity Card is crucial to ensure the clarity and accuracy of information in various applications, such as identity verification, administration, and security. This study aims to improve the digital image quality of Identity Card using the Contrast Stretching and Retinex methods. Contrast Stretching is employed to enhance image contrast, while the Retinex method is used to maintain and improve the clarity of colors and image details. The implementation of these methods is carried out using Python. The test results show that these methods are effective in improving the quality of Identity Card images, making the information on the images clearer and easier to read. This research contributes to the development of more advanced image processing technology, which can be applied to various verification and administrative needs that require high reliability.

Application of hyperspectral imaging technology to digitally protect murals in the Qutan temple

Hyperspectral imaging technology is a research hotspot in the field of cultural heritage protection. It can be used to quickly and noninvasively obtain detailed spectral information from the surfaces of cultural relics of different categories. We can intuitively analyse pigment compositions, line characteristics, painting skills and patterns using spectral information. Hyperspectral imaging has high scientific significance and application value for the protection, restoration and research of ancient murals and other cultural relics. In this study, a mural from Daheitian hall in the Qutan temple, Qinghai Province, China, was used as a sample. The hyperspectral data were acquired and analysed for several purposes. Pigment spectral matching and abundance inversion were carried out to obtain the pigment distribution. These data were enhanced by continuum removal and histogram stretching to obtain hidden information. The dark channel prior, Criminisi and Retinex methods were used to virtually restore the image of the mural. The results indicated that by using hyperspectral imaging data, the constructed pure pigment spectrum library and suitable approaches, the types and distributions of mural pigments can be quantitatively analysed, and the lines in murals can be extracted. Hyperspectral images are helpful for identifying information hidden by pigments or surface materials. Mural images can be enhanced, and hidden information can be highlighted using enhancement methods, such as continuum removal and histogram linear stretching. In addition, hyperspectral imaging data have unique advantages in the restoration of mural images, and the combination of defogging methods and image inpainting algorithms can realize the virtual restoration of mural images. In brief, hyperspectral imaging technology was found to have a highly favourable effect on pigment analysis, line extraction, information enhancement, hidden information extraction and the virtual restoration of ancient murals.

Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep Learning Techniques.

The image of expressway asphalt pavement crack disease obtained by a three-dimensional line scan laser is easily affected by external factors such as uneven illumination distribution, environmental noise, occlusion shadow, and foreign bodies on the pavement. To locate and extract cracks accurately and efficiently, this article proposes a three-stage asphalt pavement crack location and segmentation method based on traditional digital image processing technology and deep learning methods. In the first stage of this method, the guided filtering and Retinex methods are used to preprocess the asphalt pavement crack image. The processed image removes redundant noise information and improves the brightness. At the information entropy level, it is 63% higher than the unpreprocessed image. In the second stage, the newly proposed YOLO-SAMT target detection model is used to locate the crack diseases in asphalt pavement. The model is 5.42 percentage points higher than the original YOLOv7 model on mAP@0.5, which enhances the recognition and location ability of crack diseases and reduces the calculation amount for the extraction of crack contour in the next stage. In the third stage, the improved k-means clustering algorithm is used to extract cracks. Compared with the traditional k-means clustering algorithm, this method improves the accuracy by 7.34 percentage points, the true rate by 6.57 percentage points, and the false positive rate by 18.32 percentage points to better extract the crack contour. To sum up, the method proposed in this article improves the quality of the pavement disease image, enhances the ability to identify and locate cracks, reduces the amount of calculation, improves the accuracy of crack contour extraction, and provides a new solution for highway crack inspection.

Joint enhancement and denoising in electronic speckle pattern interferometry fringe patterns with low contrast or uneven illumination via an oriented variational Retinex model.

Simultaneous speckle reduction and contrast enhancement for electronic speckle pattern interferometry (ESPI) fringe patterns is a challenging task. In this paper, we propose a joint enhancement and denoising method based on the oriented variational Retinex model for ESPI fringe patterns with low contrast or uneven illumination. In our model, we use the structure prior to constrain the illumination and introduce a fractional-order differential to constrain the reflectance for enhancement, then use the second-order partial derivative of the reflectance as the denoising term to reduce noise. The proposed model is solved using the sequential method to obtain piecewise smoothed illumination and noise-suppressed reflectance sequentially, which avoids remaining noise in the illumination and reflectance map. After obtaining the refined illuminance and reflectance, we substitute the gamma-corrected illuminance into the camera response function to further adjust the reflectance as the final enhancement result. We test our proposed method on two non-uniform illumination computer-simulated and two low-contrast experimentally obtained ESPI fringe patterns. Finally, we compare our method with three other joint enhancement and denoising variational Retinex methods.

An Experiment-Based Review of Low-Light Image Enhancement Methods

Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested.

Adaptive Image Rendering Using a Nonlinear Mapping-Function-Based Retinex Model.

This paper introduces an adaptive image rendering using a parametric nonlinear mapping-function-based on the retinex model in a low-light source. For this study, only a luminance channel was used to estimate the reflectance component of an observed low-light image, therefore halo artifacts coming from the use of the multiple center/surround Gaussian filters were reduced. A new nonlinear mapping function that incorporates the statistics of the luminance and the estimated reflectance in the reconstruction process is proposed. In addition, a new method to determine the gain and offset of the mapping function is addressed to adaptively control the contrast ratio. Finally, the relationship between the estimated luminance and the reconstructed luminance is used to reconstruct the chrominance channels. The experimental results demonstrate that the proposed method leads to the promised subjective and objective improvements over state-of-the-art, scale-based retinex methods.

A detail preserving variational model for image Retinex

• A new variational model for image retinex is proposed. • The existence of minimizer for the variational model is proved. • An efficient algorithm is derived to solve the model by adding auxiliary variables. • The convergence of the numerical algorithm is proved under some assumptions. • The proposed method can be extended to other regularization and fidelity terms. In this paper, we propose a detail preserving variational model for Retinex to simultaneously estimate the illumination and the reflectance from an observed image. Most previous models use the log-transform as pretreatment which results in loss of details in reflectance. From this observation, a detail preserving variational method is proposed for better decomposition. Different from the log-transform based models, the proposed model performs the decomposition directly in the image domain. Mathematically, we prove the existence of a solution for the proposed model. Numerically, we derive an efficient iterative algorithm by utilizing alternating direction method of multipliers (ADMM) method. Experimental results demonstrate the effectiveness of the proposed method. Compared with other closely related Retinex methods, the proposed method achieves competitive results on both subjective and objective assessments.

Efficient L1-based nonlocal total variational model of Retinex for image restoration

Characteristics of an image, such as smoothness, edge, and texture, can be better preserved using the nonlocal differential operator in image processing. We establish an L1-based nonlocal total variational (NLTVL1) model based on Retinex theory that can be solved by a fast computational algorithm via the alternating direction method of multipliers. Experiential results demonstrate that our NLTVL1 method has a good performance on enhancing contrast, eliminating the influence of nonuniform illumination, and suppressing noise. Furthermore, compared with previous works, including traditional Retinex methods and variational Retinex methods, our proposed approach achieves superior performance on edge and texture preservation and needs fewer iterations on recovering the reflectance image, which is illustrated by examples and statistics.

Wavelet-Based Local Contrast Enhancement for Satellite, Aerial and Close Range Images

The methods used for image contrast enhancement in the wavelet domain have been previously documented. The essence of these methods lies in the manipulation of the image during the reconstruction process, by changing the relationship between the components that require transformation. This paper proposes a new variant based on using undecimated wavelet transform and adapting the Gaussian function for scaling the coefficients of detail wavelet components, so that the role of low coefficients in the reconstructed image is greater. The enhanced image is then created by combining the new components. Applying the Haar wavelet minimises the effects of the relationship disturbance between components, and creates a small buffer around the edge. The proposed method was tested using six images at different scales, collected with handheld photo cameras, and aerial and satellite optical sensors. The results of the tests indicate that the method can achieve comparable, or even better enhancement effects for weak edges, than the well-known unsharp masking and Retinex methods. The proposed method can be applied in order to improve the visual interpretation of remote sensing images taken by various sensors at different scales.

Contrast enhancement algorithm considering surrounding information by illumination image

We propose a contrast enhancement algorithm considering surrounding information by illumination image. Conventional contrast enhancement techniques can be classified as a retinex-based method and a tone mapping function-based method. However, many retinex methods suffer from high-computational costs or halo artifacts. To cope with these problems, efficient edge-preserving smoothing methods have been researched. Tone mapping function-based methods are limited in terms of enhancement since they are applied without considering surrounding information. To solve these problems, we estimate an illumination image with local adaptive smoothness, and then utilize it as surrounding information. The local adaptive smoothness is calculated by using illumination image properties and an edge-adaptive filter based on the just noticeable difference model. Additionally, we employ a resizing method instead of a blur kernel to reduce the computational cost of illumination estimation. The estimated illumination image is incorporated with the tone mapping function to address the limitations of the tone mapping function-based method. With this approach, the amount of local contrast enhancement is increased. Experimental results show that the proposed algorithm enhances both global and local contrasts and produces better performance in objective evaluation metrics while preventing a halo artifact.

다중크기 회색계 알고리즘 기반의 통합된 보정 계수를 이용한 바랜 영상 개선

Fading effect of old pictures and printings is shown up differently according to the ink property, temperature, humidity, illuminants, and so on. Faded image enhancement techniques based on illuminant estimation are proposed such as the gray world algorithm and white patch retinex methods. However, conventional simple operators are not suitable for enhancing faded images because partial fading effect is appeared differently. Thus, this paper presents a color enhancement algorithm based on integrating correction coefficients for faded images. First, the proposed method adopts local process by using multi-scale average mask. The coefficients for each multi-scale average mask are obtained to apply the gray world algorithm. Then, integrating the coefficients with weights is performed to calculate correction ratio for red and blue channels in the gray world assumption. Finally, the enhanced image is obtained by applying the integrated coefficients to the gray world algorithm. In the experimental results, the proposed method reproduces better colors for both wholly and partially faded images compared with the previous methods. 논문 14-39A-08-04 The Journal of Korea Information and Communications Society '14-08 Vol.39A No.08 http://dx.doi.org/10.7840/kics.2014.39A.8.459 459 ※ 이 논문은 2013년도 정부(교육과학기술부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임.(No. NRF-2013R1A2A2A01016105) First Author : Kyungpook National University, School of Electronics Engineering, kyungwj@ee.knu.ac.kr, 학생회원 ° Corresponding Author : Kyungpook National University, School of Electronics Engineering, yha@ee.knu.ac.kr, 종신회원 * Kyungpook National University, School of Electronics Engineering, straycats@ee.knu.ac.kr, 학생회원 논문번호:KICS2014-05-152, Received May 1, 2014; Revised August 8, 2014; Accepted August 8, 2014 The Journal of Korea Information and Communications Society '14-08 Vol.39A No.08

Variational Bayesian method for Retinex.

In this paper, we propose a variational Bayesian method for Retinex to simulate and interpret how the human visual system perceives color. To construct a hierarchical Bayesian model, we use the Gibbs distributions as prior distributions for the reflectance and the illumination, and the gamma distributions for the model parameters. By assuming that the reflection function is piecewise continuous and illumination function is spatially smooth, we define the energy functions in the Gibbs distributions as a total variation function and a smooth function for the reflectance and the illumination, respectively. We then apply the variational Bayes approximation to obtain the approximation of the posterior distribution of unknowns so that the unknown images and hyperparameters are estimated simultaneously. Experimental results demonstrate the efficiency of the proposed method for providing competitive performance without additional information about the unknown parameters, and when prior information is added the proposed method outperforms the non-Bayesian-based Retinex methods we compared.

최대 색차신호 표를 이용한 Retinex 영상의 컬러 향상

Retinex algorithm enhances the contrast of image through visibility improvement. However, the conventional Retinex methods may produces color distortions due to error of hue representation and over-saturation since the methods work in RGB color space. In this paper, we propose a new Retinex algorithm with color correction, which improves contrast by using MSR(Multi-Scale Retinex) working in YCbCr color space and adaptively compensates the color saturation based on the maximum color difference table. Our algorithm maps the color difference signals to the correct gamut to prevent over-saturation phenomenon by considering the correlation between luminance and hue dependent saturation. Simulations results show that the proposed method gives better color improvement compared to the conventional methods.

FUZZY MEASURE BASED ADAPTIVE METHODS FOR ILLUMINATION INVARIANT FACE RECOGNITION

Varying illumination is one of the well known and challenging problems in Face Recognition applications. Numerous methods have been proposed by researchers, but recognition performance under complex illumination is not yet satisfactory. The paper presents Fuzzy based methods to adaptively normalize illumination in face images for Face Recognition under varying illumination conditions. The paper has main two contributions: (1) Fuzzy measure based Adaptive Single-scale Retinex and (2) Fuzzy measure based Adaptive Single-scale Self Quotient Image method. Also, two more variations of these methods are presented. There are two main advantages of these methods, as compared to multi-scale Retinex and Self Quotient methods. Firstly, due to the adaptive nature of proposed methods, discontinuity in facial feature is smoothed and discontinuity due to shadows is preserved and hence performance is better. Secondly, computational complexity is reduced because of single scale 3∗3 filter instead of multi-scale filters. Rigorous experiments have been performed on CMU PIE face database and Extended Yale B face database. For determining False Acceptance Rate, 529 and 550 imposter faces are used for experiments on PIE and Yale databases respectively. Proposed methods are compared with existing methods under same experimental setup using six performance evaluation parameters. Results have shown that Fuzzy measure based methods performs well.

Evaluation of Image Corrected by Retinex Method Based on S-CIELAB and Gazing Information

The purpose of this research is to propose an effective color metric which can predict the perceptual image quality for Retinex method. In this paper, we first give a brief introduction of three kinds of typical single Retinex methods to improve the color reproduction. And then, we state the process for obtaining the observer rating value from the subjective evaluation experiment performed under the sRGB illumination condition. Next, we introduce the S-CIELAB metric and propose a new metric on the basis of S-CIELAB metric that considers the gazing information. The average S-CIELAB color differences with and without the consideration of gazing information were calculated as the objective image quality measures. The correlations between the observer rating values and the objective image quality measures were calculated. The result shows that all of the average S-CIELAB color differences based on the gazing information are better correlated to the observer rating value than the average S-CIELAB color difference over the whole area. The average S-CIELAB color difference weighted by the gazing frequency over the gazing area shows the strong correlation with the observer rating value.

Analysis of tristimulus interdifference and contextual color correction

We explore how an RGB representation can be com- puted from a spectral description of real images, with many different colors. To pass from spectral distribution to tristimulus values, sev- eral color-matching functions (CMFs) were proposed, derived from experimental setups with simplified visual conditions, considering the colors pointwise and independently from any visual context. A high interdifference is observed in cone spatial distribution between human subjects, without any corresponding significant difference in final color sensation. It is likely that a spatial compensation is per- formed by human observers that strongly decreases the subjectivity in color perception, and we ask if a similar principle should be con- sidered in digital imaging. We investigate the interdifference among some CMFs when used to compute color information in complex visual conditions, where multiple, spatially distributed different col- ors are present. This is relevant in synthetic image generation of scenes under different illuminants, computed at the spectral energy distribution at 5-nm intervals, to be converted into RGB for monitor display. The analysis of the interdifference among tristimulus colors obtained using different CMFs shows a significant decrease of the interdifference when a contextual color correction is applied, based on Von Kries or Retinex methods. © 2006 SPIE and IS&T.

Retinex in MATLAB™

Many different descriptions of Retinex methods of light- ness computation exist. We provide concise MATLAB™ implemen- tations of two of the spatial techniques of making pixel comparisons. The code is presented, along with test results on several images and a discussion of the results. We also discuss the calibration of input images and the postRetinex processing required to display the output images. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1636761)

Retinex in Matlab

Many different descriptions of retinex methods of lightness computation exist. This paper provides concise MATLAB implementations of two of the spatial techniques of making pixel comparisons. The code is presented along with test results on several images and a discussion of the results. The paper also discusses the calibration of input images and the post-retinex processing required to display the output images.