A genetic algorithm approach to color image enhancement

Color in an image is resolved into 3 or 4 color components and 2-Dimages of these components are stored in separate channels. Most of the color image enhancement algorithms are applied channel-by-channel on each image. But such a system of color image processing is not processing the original color. When a color image is represented as a quaternion image, processing is done in original colors. This paper proposes an implementation of the quaternion approach of enhancement algorithm for enhancing color images and is referred as the modified alpha-rooting by the two-dimensional quaternion discrete Fourier transform (2-D QDFT). Enhancement results of this proposed method are compared with the channel-by-channel image enhancement by the 2-D DFT. Enhancements in color images are quantitatively measured by the color enhancement measure estimation (CEME), which allows for selecting optimum parameters for processing by the genetic algorithm. Enhancement of color images by the quaternion based method allows for obtaining images which are closer to the genuine representation of the real original color.

Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two different classifiers are used with color histograms in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces across different numbers of bins, and overall performance of each bin size is compared with that of other bin sizes. The research findings show that the performance of REVC system does not improve in terms of overall sensitivity rate, when the number of bins in color histogram is increased. Moreover, this paper identifies the advantage of using HSV color space over RGB in using color histogram for REVC systems. Furthermore, findings recognize the optimum number of bins in both RGB and HSV color spaces, and ANOVA (analysis of variance) is used to analyze experimental data, which identifies the optimum color histogram bin size used for HSV color space is significantly better than that used for RGB color space in REVC systems.

In the state of the art, grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination. As these methods are applied to each RGB channel independently, imbalanced inter-channel enhancements (color distortion) can often be observed in the resulting images. On the other hand, images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring, halos, and over-enhancement. To address these problems, an improved RGB color image enhancement method is proposed for images captured under non-uniform illumination or in poor visibility, based on weighted guided image filtering (WGIF). Unlike the conventional retinex algorithm and its variants, WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component; it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization. To limit color distortion, RGB images are first converted to HSI (hue, saturation, intensity) color space, where only the intensity channel is enhanced, before being converted back to RGB space by a linear color restoration algorithm. Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination, with better visual quality and objective evaluation scores than from comparator algorithms. It is also efficient due to use of a linear color restoration algorithm.

We propose a method to enhance the color image of a low-light scene using a single sensor that simultaneously captures red, green, blue (RGB), and near-infrared (NIR) information. Typical image enhancement methods require two sensors to simultaneously capture color and NIR images. In contrast, our proposed system utilizes a single sensor but achieves accurate color image restoration. We divide the captured multispectral data into RGB and NIR information based on the spectral sensitivity of our imaging system. Using the NIR information for guidance, we reconstruct the corresponding color image based on a joint demosaicking and denoising technique. Subsequently, we restore the estimated color image iteratively using the constructed guidance image. Our experiments demonstrate the effectiveness of our method using synthetic data, and real raw data captured by our imaging system.

The development of digital techniques for color image enhancement is very important because color pictures include information that is essential in many research and application fields. Color image enhancement, however, is not a trivial problem, because it typically deals with conflicting operations such as data sharpening and smoothing. The challenging goals consist in avoiding noise amplification, chroma distortion and excessive overshoot on object edges. This paper presents a novel technique for the enhancement of RGB images that is designed to address all these issues. The proposed approach consists in a two-stage recursive operator that adopts fuzzy processing in order to gradually perform smoothing and sharpening of noisy color data. In this approach, chroma distortion is avoided by choosing vector smoothing of RGB data and by applying the sharpening to the luminance channel only. Computer simulations show that the method combines simplicity and effectiveness. It can sharpen the image details and reduce the noise without generating false colors, whereas other techniques cannot. The method is easy to use too. The behavior of the operator is easily controlled by choosing a few parameter values.

In this study, we present a method of chroma enhancement in the CIELAB color space and compare it with that in the RGB color space. Color image enhancement using the CIELAB color space has the disadvantage that the color gamut problem occurs because the conversion to the RGB color space is necessary to display the image. However, since the CIELAB color space is based on human visual perception, the quality of the resulting images is expected to be higher than that of the RGB color space. In the method using the CIELAB color space, we introduce a lookup table to reduce the calculation costs. Experiments comparing image enhancement results obtained from two color spaces are performed using several digital images.

This paper presents a luminosity conserving and contrast enhancing histogram equalization method for color images. The histogram equalization is one of the ordinary methods employed for enhancing contrast in TV and images for consumer electronics where unwanted subjective deterioration are frequently occur. Although there have been many solutions to overcome the drawback of histogram equalization, however the method is for RGB color space, which is not well suited for different color spaces. To do this, we use fuzzy set to improve histogram equalization. All RGB images are firstly transformed into different color spaces, and particular channels are applied histogram equalization process. From our 20 test LC images show that HSV color space yields the favorable results in MSE by giving the luminosity conserving ability.

Natural color image enhancement and evaluation algorithm based on human visual system

Color image enhancement refers to improving the visual quality of color images by adjusting various attributes such as brightness, contrast, sharpness, and color balance. This enhancement aims to make the images more visually appealing, precise, and easier to interpret, often by accentuating essential features or details while minimizing noise or distortion. Techniques vary from simple adjustments of brightness and contrast to sophisticated algorithms. Effective image enhancement is crucial in numerous fields like medical imaging; enhancing the medical image is vital for accurate diagnosis and treatment planning, as well as remote sensing for improved satellite and aerial imagery. In security and surveillance, enhanced clarity in footage from surveillance cameras, especially in low-light scenarios, is crucial for identifying subjects and activities. However, image enhancement faces several challenges, like noise amplification and Over-enhancement, leading to unnatural-looking images with exaggerated or distorted features. In this paper, low-exposed or night color images are considered for enhancement, and this paper introduces exposure-based recursive histogram equalization techniques along with an Energy Curve instead of the conventional histogram; the energy curve is similar to a histogram based on spatial Contextual Information of an image. The proposed methods are Recursive Exposure-based Sub-image Histogram Equalization, Recursively Separated Exposure-based Sub-image Histogram Equalization, and Exposure-based Sub-image Histogram Equalization techniques considering spatial contextual information of images using an Energy Curve to improve results. These methods were tested on several publicly available datasetswith low-contrast color images and compared with HE, BBHE, DSHE, CLAHE, ESIHE, R-ESIHE, and RS-ESIHE. The proposed techniques are evaluated using parameterslikeAMBE, PSNR, MSE, Entropy, SSIM, and FSIM. The average PSNR values for eight images (taken from TeV DICeneter https://tev.fbk.eu/resources/imageenhancement) using the techniques mentioned above were 9.10817, 20.80568, 12.82645, 10.39347, 16.27458, 15.19979, and 14.59595, respectively. In contrast, the proposed ESIHE with Energy Curve achieved a PSNR of 22.31585, outperforming other methods across multiple metrics, particularly excelling in noise reduction (PSNR), error minimization (MSE: 193.44), structural similarity (SSIM: 0.733319), and feature retention (FSIM: 0.917678). The comparison demonstrates that enhancement methods can be significantly improved by considering spatial contextual information.

In color image enhancement, gamut problem is one of the fundamental issues for practical image processing tasks. However, recent color image enhancement methods without gamut problem fail to preserve or enhance color saturation, which is an essential property for visual perception. In this paper, we propose a color image enhancement method, in which we introduce a new histogram named aggregated histogram (AgH), which is computed with R, G and B components of all pixels in a color image, and then equalize AgH to obtain an enhanced color image in a similar manner to conventional grayscale histogram equalization (GHE). As a result, the proposed method is free from gamut problem as well as GHE. Moreover, we extend the proposed AgH equalization (AgHE) method to adaptive AgHE (AAgHE), which equalizes a local histogram at each pixel of a color image. Experimental results show that the proposed methods produce better results than existing color image enhancement methods. Consequently, the proposed methods can be a good alternative to conventional color image enhancement methods without gamut problem.

Color image enhancement plays a key role in digital image processing more than grayscale image enhancement due to its wide color expressions with three perceptual attributes including hue, saturation and intensity of an image. It has been a popular research since many recent years but it is still an open issue in order to enhance the color for low intensity, blurred or incomplete images, etc. This paper proposes a color enhancement algorithm with an affine algorithm for enhancing illumination in gamut preserving color image enhancement. The proposed method firstly uses non-linear sigmoidal function for luminance enhancement while an improved histogram specification model is used to preserve gamut of an input image. The proposed algorithm is experimented with low intensity images obtained from standard image datasets which are compared with quite popular image enhancement methods. According to the experimental results, the proposed method produces less error rate than other models.

Steganography is one of the methods used for secret communication. Steganography attempts to hide the existence of the information. The object used to hide the secret information is called as cover object. Images are the most popular cover objects used for steganography. Different techniques have to be used for color image steganography and grey scale image steganography since they are stored in different ways. Color image are normally stored with 24 bit depth and grey scale images are stored with 8 bit depth. Color images can hold large amount of secret information since they have three color components. Different color spaces namely RGB (Red Green Blue), HSV (Hue, Saturation, Value), YUV, YIQ, YCbCr (Luminance, hrominance) etc. are used to represent color images. Color image steganography can be done in any color space domain. In this paper color image steganography in RGB and YCbCr domain are compared. The secret information considered is grey scale image. Since RGB is the common method of representation, hiding secret information in this format is not secure.

The living softwood tree forms compression wood to compensate for external loads during growth, which creates wood fibers with higher longitudinal shrinking and swelling than normal wood at moisture content changes. This is often the cause of undesirable warping of sawn wood products after drying. An automatic detection of severe compression wood is thus useful to reject unwanted pieces. Detection in green condition is often preferred in a sawmill while detection in dry condition is needed in other applications. Three different non- destructive scanning methods were evaluated on both green and dry wood surfaces. The methods used were RGB (red, green, blue) color scanning, tracheid-effect scanning and x-ray scanning. The color and x-ray methods were evaluated on Southern yellow pine lumber, while the tracheid-effect scanning was tested on Norway spruce. For scanning in green condition detection of compression wood was good using the tracheid-effect and color scanning. X-ray scanning was not useful because of the uneven moisture distribution in green lumber. After drying the result changes, tracheid-effect and x-ray scanning have good detection ability while RGB color does not provide sufficient information for reliable detection.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Constant increase in urban road traffic is forcing traffic authorities to confront unavoidable congestion problems these days. This creates new challenges for city planners to generate traffic routes with better designs and improve the existing roads. In order to analyze and improve traffic routes, there is a need to understand movement patterns of major vehicular traffic inside the city. Origin Destination(OD) estimation is one such method for understanding the movement patterns. The objective is to find an optimal OD matrix for city traffic, which is a subtle process for classic algorithms. The solution to this problem is Genetic Algorithm (GA) approach which is a search heuristics to find optimal solution from a set of random solutions. A Genetic Algorithm approach has been developed to find an optimal OD matrix, showing the actual travel patterns of significant number of vehicles inside the city. Once the best OD matrix is obtained, it yields a big opportunity for traffic planners to analyze and improve traffic scenario. Genetic Algorithm can play a major role in solving complex problems. In this paper, one such problem of estimating OD matrix of a city is discussed and implemented. In conjunction to genetic algorithm, Mean Absolute Percentage Error(MAPE) has been used as a fitness criteria for finding the optimal OD matrix.

This study deals with estimation of the total and industrial sector electricity consumption based on genetic algorithm (GA) approach, and then proposes two scenarios to project future consumptions. Total electricity consumption is estimated based on gross national product (GNP), population, import and export figures of Turkey. Industrial sector electricity is calculated based on the GNP, import and export figures. Three forms of the genetic algorithm electricity demand (GAED) models for the total and two forms for the industrial electricity consumption are developed. The best-fit GAED model in terms of total minimum relative average errors between observed and estimated values is selected for future demand estimation. ‘High- and low-growth scenarios’ are proposed for predicting the future electricity consumption. Results showed that the GAED estimates the electricity demand in comparison with the other electricity demand projections. The GAED model plans electricity demand of Turkey until 2020. Copyright © 2005 John Wiley & Sons, Ltd.