Color Image Enhancement Method Research Articles

Low-light image enhancement based on Retinex theory for beam-splitting prism system

The existing color image enhancement methods are utilized for the most part to directly improve the color image that is obtained through the use of a Bayer array camera, whereas the color image that is obtained through the use of a prism camera is the result of the fusion of three primary color images that have been collected independently. As a result, a method for enhancing color photographs based on three images of primary colors that have a low level of light has been proposed. In this paper, we first transfer RGB to HSV space for image enhancement. In the V channel, we use guided filtering combined with improved SSR method to better preserve edge information. Then the S channel is adjusted adaptively to make the image look more natural. Finally, the image is transferred from HSV space back to RGB space. Compared with other traditional methods, our method achieves better results in PSNR, SSIM and NIQE, and has less computational complexity.

Color Image Enhancement Focused on Limited Hues.

Some color images primarily comprise specific hues, for example, food images predominantly contain a warm hue. Therefore, these hues are essential for creating delicious impressions of food images. This paper proposes a color image enhancement method that can select hues to be enhanced arbitrarily. The current chroma is considered such that near achromatic colors are not over-enhanced. The effectiveness of the proposed method was confirmed through experiments using several food images.

Simple low-light image enhancement based on Weber–Fechner law in logarithmic space

In an environment with poor illumination, such as indoor, night, and overcast conditions, the image information can be seriously lost, which affects the visual effect and degrades the performance of machine systems. However, existing methods such as retinex-based method, dehazing model-based method, and machine learning-based method usually have high computational complexity or are prone to color distortion, noise amplification, and halo artifacts. To balance the enhancement effect and processing speed, this paper applies the Weber–Fechner law to the grayscale mapping in logarithmic space and proposed an adaptive and simple color image enhancement method based on the improved logarithmic transformation. In the framework, the brightness component is extracted from the scene of the low-light image using Gaussian filtering after color space conversion. The image is logarithmically transformed by adaptively adjusting the parameters of the illumination distribution to improve the brightness of the image. The color saturation is hence compensated. The proposed algorithm adaptively reduces the impact of non-uniform illumination on the image, and the enhanced image is clear and natural. Our experimental results demonstrate improved performance to the existing image enhancement approaches.

Low and non-uniform illumination color image enhancement using weighted guided image filtering

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.

Enhancement of UAV-Aerial Images Using Weighted Differential Evolution Algorithm

Depending on technological developments, the use of Unmanned Aerial Vehicles (UAVs) is increasing day by day and is a valuable source of data for different applications. Generally, low-cost and lightweight non-metric digital cameras are used in UAV systems. During the data collection phase, exposure parameters such as camera shutter speed, aperture value, ISO value, and various weather and light conditions have significant effects on image quality. Image enhancement methods can be used to increase image quality in accordance with the desired purpose. In this study, image enhancement is considered as an optimization problem and Weighted Differential Evolution (WDE) Algorithm is used to solve it. The image quality is enhanced by using an objective function in which performance measures of entropy value, sum of edge density and number of edge pixel are maximized. In the proposed color image enhancement method, aerial images defined in RGB color space are transformed into HSV color space images. the brightness component (V) of HSV color space is modified for image improvement with WDE algorithm. The performance of the proposed method has been compared with other existing techniques such as histogram equalization, linear contrast stretching and evolutionary computing-based image enhancement method like Artificial Bee Colony (ABC) Algorithm in terms of fitness value and image quality.

Adaptive Aggregated Histogram Equalization for Color Image Enhancement without Gamut Problem

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.

Digital measurement of 2D and 3D cracks in sandstones through improved pseudo color image enhancement and 3D reconstruction method

SummaryAn integrated approach of improved pseudo color image enhancement (IPCE) method and three‐dimensional (3D) reconstruction technology is proposed. The evolution characteristics of two‐dimensional (2D) and 3D spatial cracks in X‐ray computed tomography (CT) images of sandstones subjected to triaxial compression with confining pressure of 10, 20, and 30 MPa are investigated. The equations of crack width, length, and dip angle are established based on the proposed digital damage ratio. Based on the pseudo color images, point cloud maps with roughness and spatial images of cracks are investigated. The numerical results show that this proposed method is effective to study and understand the fracturing properties of rocks.

Hue-preserving image enhancement in CIELAB color space considering color gamut

In this paper, we propose a color image enhancement method that uses the CIELAB color space to improve the quality of degraded images for human visual perception. In our method, the lightness of a color image is enhanced, based on gamma correction, and the hue and chroma are preserved in the CIELAB color space. During this process, color gamut problem may occur while inversely converting the enhanced CIELAB color components to the RGB color space. Therefore, we introduce a color gamut adjustment to cope with this problem. In the color gamut adjustment, the chroma is changed while preserving the enhanced lightness and the hue so that the inversely converted CIELAB color components will exist inside the color gamut of the RGB color space. To illustrate the effectiveness of this method, experiments, using several digital images, are conducted.

Alpha-Rooting Color Image Enhancement Method by Two-Side 2-D Quaternion Discrete Fourier Transform Followed by Spatial Transformation

In this paper a quaternion approach of enhancement method is proposed in which color in the image is considered as a single entity. This new method is referred as the alpha-rooting method of color image enhancement by the two-dimensional quaternion discrete Fourier transform (2-D QDFT) followed by a spatial transformation. The results of the proposed color image enhancement method are compared with its counterpart channel-by-channel enhancement algorithm by the 2-D DFT. The image enhancements are quantified to the enhancement measure that is based on visual perception referred as the color enhancement measure estimation (CEME). The preliminary experiment results show that the quaternion approach of image enhancement is an effective color image enhancement technique.

A Novel Color Image Enhancement Method by the Transformation of Color Images to 2-D Grayscale Images

A novel method of color image enhancement is proposed, in which three or four color channels of the image are transformed to one channel 2-D grayscale image. This paper describes different models of such transformations in the RGB and other color models. Color image enhancement is achieved by enhancing first the transformed grayscale image and, then, transforming back the grayscale image into the colors. The color image enhancement is done on the transformed 2-D grayscale image rather than on the color image. New algorithms of color image enhancement are described in both frequency and time domains. The enhancement by this novel method shows good results. The enhancement of the image is measured with respect to the metric referred to as the Color Enhancement Measure Estimation (CEME).

Saturation improvement in hue-preserving color image enhancement without gamut problem

Abstract This study proposes a method to improve the saturation of colors during hue-preserving color image enhancement without the gamut problem. First, the color of each pixel in an input image is projected onto one of three bisecting planes or bisectors of an RGB color cube to increase saturation. Then, that projected color is transformed into a target color with a designated luminance and the same hue as the original color. Experimental results show that the proposed method can enhance color images better than can recent hue-preserving color image enhancement methods.

RGB Color Cube-Based Histogram Specification for Hue-Preserving Color Image Enhancement

A large number of color image enhancement methods are based on the methods for grayscale image enhancement in which the main interest is contrast enhancement. However, since colors usually have three attributes, including hue, saturation and intensity of more than only one attribute of grayscale values, the naive application of the methods for grayscale images to color images often results in unsatisfactory consequences. Conventional hue-preserving color image enhancement methods utilize histogram equalization (HE) for enhancing the contrast. However, they cannot always enhance the saturation simultaneously. In this paper, we propose a histogram specification (HS) method for enhancing the saturation in hue-preserving color image enhancement. The proposed method computes the target histogram for HS on the basis of the geometry of RGB (rad, green and blue) color space, whose shape is a cube with a unit side length. Therefore, the proposed method includes no parameters to be set by users. Experimental results show that the proposed method achieves higher color saturation than recent parameter-free methods for hue-preserving color image enhancement. As a result, the proposed method can be used for an alternative method of HE in hue-preserving color image enhancement.

Modified Alpha-Rooting Color Image Enhancement Method on the Two Side 2-D Quaternion Discrete Fourier Transform and the 2-D Discrete Fourier Transform

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.

一种基于FPGA的彩色图像实时增强方法

一种基于FPGA的彩色图像实时增强方法

Color Image Enhancement Based on Adaptive Nonlinear Curves of Luminance Features

This paper proposes an image-dependent color image enhancement method that uses adaptive luminance enhancement and color emphasis. It effectively enhances details of low-light regions while maintaining well-balanced luminance and color information. To compare the structure similarity and naturalness, we used the tone mapped image quality index (TMQI). The proposed method maintained better structure similarity in the enhanced image than did the space-variant luminance map (SVLM) method or the adaptive and integrated neighborhood dependent approach for nonlinear enhancement (AINDANE). The proposed method required the smallest computation time among the three algorithms. The proposed method can be easily implemented using the field-programmable gate array (FPGA), with low hardware resources and with better performance in terms of similarity.

Color Image Enhancement using Edge Based Histogram Equalization

Image enhancement is one of the major research areas in the field of digital image processing. The sole objective of this domain is to enhance the quality of a poor contrast image. As a result, the final processed image becomes much more understandable than the original one. A new edge based histogram equalization method is proposed in this study. A high pass filter is used to detect edges with a help of an appropriate gradient operator. In general, a convolution mask is used for this kind of area processes like filtering. This proposed method increases the quality of the poor contrast area. This method does not create an impact on brighter area in the given input image. A few portion of the poor contrast is raised and few others contrast are being reduced. The simulation results of this study are compared with the conventional histogram equalization. Our experimental results show that the Peak Signal-to-Noise Ratio (PSNR) is better than the regular Histogram Equalization (HE) method. All simulation results are obtained using Matlab simulation software tool with standard color test images. Color image enhancement methods are applicable in the areas such as iris recognition, digital photography, remote sensing, biology, medicine, geophysics and microarray techniques, etc.

Color Enhancement in Endoscopic Images Using Adaptive Sigmoid Function and Space Variant Color Reproduction

Modern endoscopes play an important role in diagnosing various gastrointestinal (GI) tract related diseases. The improved visual quality of endoscopic images can provide better diagnosis. This paper presents an efficient color image enhancement method for endoscopic images. It is achieved in two stages: image enhancement at gray level followed by space variant chrominance mapping color reproduction. Image enhancement is achieved by performing adaptive sigmoid function and uniform distribution of sigmoid pixels. Secondly, a space variant chrominance mapping color reproduction is used to generate new chrominance components. The proposed method is used on low contrast color white light images (WLI) to enhance and highlight the vascular and mucosa structures of the GI tract. The method is also used to colorize grayscale narrow band images (NBI) and video frames. The focus value and color enhancement factor show that the enhancement level in the processed image is greatly increased compared to the original endoscopic image. The overall contrast level of the processed image is higher than the original image. The color similarity test has proved that the proposed method does not add any additional color which is not present in the original image. The algorithm has low complexity with an execution speed faster than other related methods.

Quaternion Fourier transform based alpha-rooting method for color image measurement and enhancement

The aim of this paper is to present a transform-based framework for color image enhancement which processes all three color components (R,G,B) simultaneously. In this framework, the image enhancement procedure is supported by a two-dimensional discrete quaternion Fourier transform and the new multi-frequency band α-rooting concept. Moreover, we introduce a simple measure to evaluate the quality of enhanced color images. Additionally, we demonstrate the performance of the proposed algorithms against recently proposed methods which separately process color channels. Preliminary results show that the presented algorithm out-performs other enhancement methods such as the traditional Fourier transform-based alpha-rooting and the multi-scale Retinex algorithms. The proposed algorithms are fast and simple in implementation and design which makes them very practical for image enhancement.

CONTRAST ENHANCEMENT OF COLOR IMAGES USING IMPROVED RETINEX METHOD

Color images provide large information for human visual perception compared to grayscale images. Color image enhancement methods enhance the visual data to increase the clarity of the color image. It increases human perception of information. Different color image contrast enhancement methods are used to increase the contrast of the color images. The Retinex algorithms enhance the color images similar to the scene perceived by the human eye. Multiscale retinex with color restoration (MSRCR) is a type of retinex algorithm. The MSRCR algorithm results in graying out and halo artifacts at the edges of the images. So here the focus is on improving the MSRCR algorithm by combining it with contrast limited adaptive histogram equalization (CLAHE) using image.

Color Image Contrast Enhancement using Daubechies D4 Wavelet and Luminance Analysis

enhancement in color images. The wavelet transform decomposes an image into bands that vary in spatial frequency and orientation. The HSV color model is well suited for color image enhancement methods even though RGB color space is the predominant one. By using Daubechies D4 wavelet transformation and HSV color model, a novel method of color image enhancement based on luminance adjustment is proposed here. The proposed method not only enables approximating digital signals in a better way but also it approximates highly non-linear digital signals. The experimental results showed that this new method can enhance color images effectively.