Non-uniform Illumination Images Research Articles

Fuzzy Transform for Contrast Enhancement of Nonuniform Illumination Images

Contrast enhancement of nonuniform illumination image is a challenging area of image processing. Retinex-based algorithms have been widely adopted to enhance nonuniform illumination images for decades. But the idea of enhancing reflectance layer by removing the illumination is quite unreasonable and often results in poor ambiance. In this letter, we propose an algorithm to regularize the image illumination by utilizing the fuzzy transform in the luminance domain. In order to preserve the color and quality of the enhanced image, proposed algorithm is performed on value component of HSV space. The experimental results conducted on benchmark test images with qualitative and quantitative measures demonstrate the significance of the proposed algorithm. In addition, compared to the existing state-of-art methods, the proposed algorithm produces naturalness-preserved high-quality enhanced image with low computational cost.

Naturalness Preserved Nonuniform Illumination Estimation for Image Enhancement Based on Retinex

Illumination estimation is important for image enhancement based on Retinex. However since illumination estimation is an ill-posed problem it is difficult to achieve accurate illumination estimation for nonuniform illumination images. The conventional illumination estimation algorithms fail to comprehensively take all the constraints into the consideration such as spatial smoothness sharp edges on illumination boundaries and limited range of illumination. Thus these algorithms cannot effectively and efficiently estimate illumination while preserving naturalness. In this paper we present a naturalness preserved illumination estimation algorithm based on the proposed joint edge-preserving filter which exploits all the abovementioned constraints. Moreover a fast estimation is implemented based on the box filter. Experimental results demonstrate that the proposed algorithm can achieve the adaptive smoothness of illumination beyond edges and ensure the range of the estimated illumination. When compared with other state-of-the-art algorithms it can achieve better quality from both subjective and objective aspects.

Adaptive Fuzzy Exposure Local Contrast Enhancement

Numerous factors, such as illumination condition, can affect image quality. Local contrast enhancement is an approach for improving the local visibility detail of an image by increasing the contrast in local regions. Recently, researchers have shown an interest in solving the issue of non-uniform illumination. However, all the studies failed to improve the contrast of the non-uniform illumination and low-contrast images locally and regionally by individually focusing on each region and improving its contrast. The contrast of each region will be enhanced using a new local contrast enhancement technique called adaptive fuzzy exposure local contrast enhancement (AFELCE). The proposed AFELCE method is specifically designed to enhance the contrast by using specific algorithms for different regions. The proposed AFELCE technique successfully improves the contrast of 300 low-contrast and non-uniform illumination images taken from three different databases, namely, standard, underwater (UW), and microscopic human sperm (MHS) images. The proposed AFELCE method outperforms the state-of-the-art methods in terms of quality and quantity. Qualitatively, the proposed AFELCE method has successfully enhanced the contrast of the images by producing more uniform illumination images with high contrast than the other methods. Quantitatively, the proposed AFELCE method produces the highest average of entropy (E), measurement of enhancement (EME), and universal image quality index (UIQI) for the standard image database with the values of 7.582, 42.75, and 0.94, respectively. Similar results are obtained for the UW image database, where the proposed method produces the highest average of E, EME, and UIQI values with 7.124, 41.13, and 0.89, respectively. For the MHS image database, AFELCE produces the highest values for E and EME with the values of 7.602 and 42.51, respectively.

Adaptive enhancement for nonuniform illumination images via nonlinear mapping

Nonuniform illumination images suffer from degenerated details because of underexposure, overexposure, or a combination of both. To improve the visual quality of color images, underexposure regions should be lightened, whereas overexposure areas need to be dimmed properly. However, discriminating between underexposure and overexposure is troublesome. Compared with traditional methods that produce a fixed demarcation value throughout an image, the proposed demarcation changes as local luminance varies, thus is suitable for manipulating complicated illumination. Based on this locally adaptive demarcation, a nonlinear modification is applied to image luminance. Further, with the modified luminance, we propose a nonlinear process to reconstruct a luminance-enhanced color image. For every pixel, this nonlinear process takes the luminance change and the original chromaticity into account, thus trying to avoid exaggerated colors at dark areas and depressed colors at highly bright regions. Finally, to improve image contrast, a local and image-dependent exponential technique is designed and applied to the RGB channels of the obtained color image. Experimental results demonstrate that our method produces good contrast and vivid color for both nonuniform illumination images and images with normal illumination.

Bi-histogram modification method for non-uniform illumination and low-contrast images

Researchers face non-uniform illumination and low-contrast image challenges during the image-processing stage. A new contrast enhancement method is proposed in this paper to address these challenges. The proposed method first separates the dark and bright regions of an image. Then, these regions are enhanced using two new enhancers, namely, dark and bright. Modified clipped histogram equalization is then applied for contrast enhancement. Finally, the details of the image are added back into the illumination-corrected and contrast-enhanced image for the final output image. Visually, the proposed method successfully produces better images with more uniform illumination and better contrast than the state-of-the-art methods. This claim is supported by quantitative analysis that shows that the proposed method produces the best average measure of enhancement, natural image quality evaluator, and entropy values of 797 test images compared with other state-of-the-art methods.

Mission-critical monitoring based on surround suppression variational Retinex enhancement for non-uniform illumination images

In this letter, a surround suppression variational Retinex enhancement algorithm (SSVR) is proposed for non-uniform illumination images. Instead of a gradient module, a surround suppression mechanism is used to provide spatial information in order to constrain the total variation regularization strength of the illumination and reflectance. The proposed strategy preserves the boundary areas in the illumination so that halo artifacts are prevented. It also preserves textural details in the reflectance to prevent from illumination compression, which further contributes to the contrast enhancement in the resulting image. In addition, strong regularization strength is enforced to eliminate uneven intensities in the homogeneous areas. The split Bregman optimization algorithm is employed to solve the proposed model. Finally, after decomposition, a contrast gain is added to reflectance for contrast enhancement, and a Laplacian-based gamma correction is added to illumination for prevent color cast. The recombination of the modified reflectance and illumination become the final result. Experimental results demonstrate that the proposed SSVR algorithm performs better than other methods.

Enhancer-based contrast enhancement technique for non-uniform illumination and low-contrast images

Digital imaging is widely applied in medical, surveillance, machine vision, and other fields. Occasionally, limited light sources during image acquisition process cause non-uniform illumination and low contrast images. Non-uniform illumination and low-contrast image are challenges faced by researchers during the image processing stage. In this paper, a new algorithm called Enhancer-based Contrast Enhancement (EBCE) is proposed to enhance non-uniform illumination and low-contrast image to produce uniform illumination and improve the contrast of images. The proposed method initially derives two enhancers, namely, bright enhancer and dark enhancer from a blurred input image. The bright and dark enhancers respectively enhance the bright and dark regions of the given input image. To enhance the contrast of the image, limited histogram equalization is applied to both regions. Finally, an enhancement ratio is proposed to control the enhancement level of the images. Compared with state-of-the-art methods, the proposed EBCE method successfully produces better images. Visually, the EBCE method produces the best images with more uniform illumination and better contrast. The method produces the best EME, entropy, and NIQE values when applied to 450 test images.

Structural compensation enhancement method for nonuniform illumination images.

A structural compensation enhancement method is proposed to resolve the issue of nonuniform illumination image enhancement. A logarithmic histogram equalization transformation (LHET) is developed for improving the contrast of image and adjusting the luminance distribution. A structural map of illumination compensation is produced with a local ambient light estimation filter. The enhanced image is obtained by nonlinearly fusing the LHET result, reflection component, and structural map of illumination compensation. Unlike existing techniques, the proposed method has the ability of two-way adjustment for brightness. Furthermore, the proposed method can effectively enhance the nonuniform illumination images with a balance between visibility and naturalness. Extensive experimental comparisons with some state-of-the-art methods have shown the superior performance of the proposed method.

Adaptive fuzzy intensity measure enhancement technique for non-uniform illumination and low-contrast images

A new enhancement technique based on fuzzy intensity measure is proposed in this study to address problems in non-uniform illumination and low contrast often encountered in recorded images. The proposed algorithm, namely adaptive fuzzy intensity measure, is capable of selectively enhancing dark region without increasing illumination in bright region. A fuzzy intensity measure is calculated to determine the intensity distribution of the original image and distinguish between bright and dark regions. Image illumination is improved, whereas local contrast of the image is increased to ensure detail preservation. Implementation of the proposed technique on grayscale and color images with non-uniform illumination images shows that in most cases (i.e., except for processing time), the proposed technique is superior compared with other state-of-the-art techniques. The proposed technique produces images with homogeneous illumination. In addition, the proposed method is computationally fast (i.e., \(<\)1 s) and thus can be utilized in real-time applications.

A Fast and Reliable Corner Detector for Non-Uniform Illumination Mineshaft Images

AbstractWe propose a fast and reliable corner detector that can detect corners under non-uniform illumination and fuzzy mineshaft images effectively. First, we presented an inner mask that used only four pixels to determine the flat and corner regions of an image, which could eliminate unnecessary computation of flat regions, thus reducing computing cost. Second, we separated the corner regions into background and foreground and computed the separate corner threshold to settle non-uniform illumination. Third, we proposed a fast corner-detection algorithm to compute the nucleus continuous contributive segment based on the corner state. Finally, we proposed two effective methods to remove the false corners. Experimental results showed that our approach has a better detection quality and is less time consuming than three other algorithms on an artificial image, a noisy image, and non-uniform images and could meet the real-time requirement of mineshaft applications.

Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non-uniform illumination images cannot be effectively preserved. However, naturalness is essential for image enhancement to achieve pleasing perceptual quality. In order to preserve naturalness while enhancing details, we propose an enhancement algorithm for non-uniform illumination images. In general, this paper makes the following three major contributions. First, a lightness-order-error measure is proposed to access naturalness preservation objectively. Second, a bright-pass filter is proposed to decompose an image into reflectance and illumination, which, respectively, determine the details and the naturalness of the image. Third, we propose a bi-log transformation, which is utilized to map the illumination to make a balance between details and naturalness. Experimental results demonstrate that the proposed algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.

A new binarization method for non-uniform illuminated document images

An original binarization method for document images acquired under non-uniform illumination conditions was proposed in this paper. The Curvelet transform and Otsu's method were combined to binarize the non-uniform illuminated images. The non-uniform illumination image was decomposed by the Curvelet. And, the Curvelet coefficients were enhanced by nonlinear functions. Then, the reconstructed image was processed by Otsu's binarization method. The experiment results of our and the others methods were shown and discussed.

Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images

This paper presents a new enhancement technique using the fuzzy set theory for low contrast and nonuniform illumination images. A new parameter called the contrast factor which will provide information on the difference among the gray-level values in the local neighborhood is proposed. The contrast factor is measured by both local and global information to ensure that the fine details of the degraded image are enhanced. This parameter is used to divide the degraded image into bright and dark regions. The enhancement process is applied on gray-scale images wherein the modified Gaussian membership function is employed. The process is performed separately according to the image’s respective regions. The performance of the proposed method is comparable with other state-of-the-art techniques in terms of processing time. The proposed method exhibits the best performance and defeats other methods in terms of preserving brightness and details without amplifying existing noises.

Support-vector-machine-based method for automated steel bridge rust assessment

Abstract Computerized methods have been used for structure health monitoring and defect recognition in the civil engineering field for many years. However, there are still non-uniform illumination problems that require more research efforts to resolve. In view of this, a new support-vector-machine-based rust assessment approach (SVMRA) is developed in this research for steel bridge rust recognition. SVMRA combines Fourier transform and support vector machine to provide an effective method for non-uniformly illuminated rust image recognition. After comparison with the popular simplified K-means algorithm (SKMA) and BE-ANFIS, it is shown that the proposed SVMRA performs more effectively in dealing with non-uniform illumination and rust images of red- and brown-color background over SKMA and BE-ANFIS.

An illumination-independent edge detection and fuzzy enhancement algorithm based on wavelet transform for non-uniform weak illumination images

An illumination-independent edge detection and fuzzy enhancement algorithm based on wavelet transform is proposed to extract edges out of the non-uniform weak illumination image. Firstly, to work out the illumination-independent edge detection method based on wavelet transform, the illumination-reflection image formation model and CCD camera imaging formula are introduced. In succession, the pixels’ wavelet module coefficients in the local edge area of the image are analyzed and compared, and the illumination-independent edge detection formula insensitive to illumination variation is presented. Then, for increasing the difference between edges, background and noise, the fuzzy enhancement operator which takes not only the wavelet module magnitude into account, but also the wavelet gradient direction is designed. Finally, single pixel edges are taken out of images according to edge pixels’ characteristic after wavelet transform. Through synthetic and real images experiments, this edge detection method’s performance for the non-uniform weak illumination images is analyzed and compared with other two edge detection methods quantitatively and qualitatively. The experiment result proves the edge detection method works well for the uneven gray and low contrast images.

Image Thresholding Computation Using Atanassov’s Intuitionistic Fuzzy Sets

In this paper, a new thresholding technique using Atanassov’s intuitionistic fuzzy sets (A-IFSs) and restricted dissimilarity functions is introduced. In recent years, various thresholding techniques ([18, 24]) based on fuzzy set theory have been introduced to overcome the problem of non-uniform illumination and inherent image vagueness. In this paper we analyze this task and propose a new method for handling the grayness ambiguity and vagueness during the process of threshold selection.