Color Image Enhancement Algorithm Research Articles

Low-light True Color Image Enhancement Algorithm Based on Adaptive Truncation Simulation Exposure and Unsupervised Fusion

Low-light True Color Image Enhancement Algorithm Based on Adaptive Truncation Simulation Exposure and Unsupervised Fusion

A two‐stage hue‐preserving and saturation improvement color image enhancement algorithm without gamut problem

A two‐stage hue‐preserving and saturation improvement color image enhancement algorithm without gamut problem

Analysis of Graphic Design Based on AI Interaction Technology.

The naive Bayes classification algorithm is used to determine the plane feature vector, and the color image enhancement algorithm based on the visual characteristics is used to improve the local contrast of the plane visual image. In addition, based on the visual perception intensity of each region of the interactive interface and the importance of visual perception elements in the edge contour, a hierarchical optimization model of graphical HMI is established, which is solved by the genetic algorithm. Finally, it has been proved that this system has high efficiency in image color processing, and the image enhancement effect is better, which meets the requirements of human visual perception. The contribution of this study lies in the design of the human-computer interface plane visual image color enhancement system, so as to improve the visual effect of plane visual image color.

Color image perception based on stochastic spiking neural network

Our aim is to present an interpretable algorithm for enhancing low-illuminance color image based on the principle of stochastic resonance and the fundamental biophysical process of human brain perceiving object color. To this end, the phenomenon of stochastic resonance in a conductance-based integrate-and-fire neuronal network is first explored, with the effect of firing threshold, synaptic weight and the population size on the signal-to-noise ratio revealed, and the firing threshold is recognized as the key parameter for the resonance effects. And then, a color image enhancement algorithm, where the peak signal-to-noise ratio and the natural image quality evaluator are adopted as quantifying indexes, is developed by combining the stochastic spiking neuronal network and the involved biophysical process relating to visual perception. Note that the enhanced image is aperiodic, thus in order to optimize the performance of the algorithm, an illuminance distribution based threshold strategy is given by us for the first time. The numerical tests show that the algorithm has good enhancement performance and stability. We wish this algorithm could be applied to relevant signal processing fields such as military detection and medical image preprocessing.

Low illumination color image enhancement based on Gabor filtering and Retinex theory

Aiming at the shortcomings of traditional Retinex image enhancement algorithms, such as poor texture detail retention, halo, over-enhancement and hue mutation, a low-illuminance color image enhancement algorithm based on Gabor filter and Retinex theory is proposed. The algorithm extracts the luminance I component from the HSI color space of the original image, and then performs MSRCR (Retinex algorithm for color restoration) enhancement on the luminance I component to obtain an enhanced luminance I component and color reproduction image. On the other hand, the original image is enhanced by a SSR (Single Scale Retinex Algorithm) based on the Gabor filter in the RGB color space to obtain an enhanced image with better texture and edge details. Then, the two images enhanced in two different ways are weighted and merged to obtain the final enhanced image. This algorithm is compared with the SSR algorithm based on Gamma correction, the MSR (multi-scale Retinex algorithm) based on bilateral filtering and the improved MSRCR algorithm. Taking mean square error, information entropy, and average gradient as evaluation indicators, the experimental results show that the image information processed by this algorithm is rich in color, rich in color, and color is closer to the original image, effectively reducing the occurrence of halo and excessive enhancement. This algorithm has certain reference significance for the enhancement of low-light color images.

An adaptive enhancement method for low illumination color images

In order to effectively improve the visual effect and image quality of color images under low illumination conditions, we propose an image enhancement method based on HSV and CIEL*a*b* color spaces for adaptively enhancing color image under low illumination conditions. The proposed method takes into account the characteristics of low illumination color images, and has the strategies of contrast, brightness enhancement, and color saturation correction. We utilize our proposed adaptive chaotic particle swarm optimization algorithm in this paper combined with gamma correction to improve the overall brightness of the image, and generate the best brightness adjustment effect in the proposed algorithm. In addition, our improved adaptive stretching function is used to enhance the image saturation. The experimental results show that compared with other traditional and latest color image enhancement algorithms, the proposed algorithm significantly enhances the visual effect of the low illumination color images. It can not only improve the contrast of low illumination color images and avoid color distortion, but also effectively improve the brightness of the image and provide more detail enhancement while maintaining the naturalness of the image.

Fuzzy dissimilarity contextual intensity transformation with gamma correction for color image enhancement

The color image enhancement algorithm proposed here yields an improvement of the image data that suppresses undesired distortions or enhances some image features and convert an image to a format better suited to machine processing. The proposed Fuzzy Dissimilarity Contextual Intensity Transformation with Gamma Correction (FDCIT-GC) consists of following stages. At first, Fuzzy Dissimilarity Histogram (FDH) is constructed from the input image. It provides the mean dissimilarity value of each intensity level present in the input image. FDH is followed by clipping in order to restricts the over enhancement rate. In order to achieve better display fidelity rendition quality, Gamma Correction (GC) is applied. To restore the natural characteristics of the image, Contextual Intensity Transformation (CIT) is applied at next to get final enhanced images. Various color images from different database are experimented and the performance of the proposed FDCIT-GC algorithm is compared with several existing methods both subjectively and objectively. Test results demonstrate that the proposed algorithm achieves better outputs than other existing techniques.

Color image enhancement with high saturation using piecewise linear gamut mapping

Most of the color image enhancement algorithms are implemented in two stages: gray scale image enhancement, which finds the target intensity, and then gamut mapping of the original color coordinates to the target. Therefore, hue preserving gamut mapping is an essential and crucial step, which influences colorfulness. In conventional color mapping methods, color saturation is reduced after intensity modification, which deteriorates subjective image quality. In this paper, a new color enhancement algorithm resulting in high color saturation is proposed. The proposed method employs multiplicative and additive color mapping to improve color saturation without clipping of a color component for increased target intensity as well as decreased cases. This new scheme is fast and effective, therefore, it can be employed to real time applications such as video signal processing.

Color image enhancement based on local spatial homomorphic filtering and gradient domain variance guided image filtering

Image quality is affected by various factors, especially low illumination. We propose an image color enhancement method to improve the brightness and quality of images. First, we apply local spatial homomorphic filtering to images to improve the brightness and avoid edge block effect as well as image size limitation at the same time. Second, gradient domain variance guided image filtering is proposed for restrain noise amplification in image enhancement, which has better denoising and edge preservation. The above two algorithms are applied to the V-channel in the hue, saturation, value space to form a color image enhancement algorithm. This method not only improves the brightness and quality of low-illumination images but also avoids noise amplification after image enhancement. The experimental results prove that our proposed algorithm is superior to other methods in improving brightness and noise removal of low-illumination images.

Color image enhancement based on adaptive multi-scale morphological unsharpening filter

In order to overcome the problem in Retinex algorithm that it is possible to cause image detail loss by using Gauss function to estimate illumination, in this paper, we propose a color image enhancement algorithm based on multi-scale morphology unsharp method combined with Retinex. Firstly, the color image is converted from RGB space to HSV space. Then, the S component is enhanced by adaptive logarithmic transformation to improve its visual characteristics and make it consistent with human visual system. The method of multi-scale morphological unsharpening is adopted to estimate the V component, and the multi-scale weights are adaptively selected according to the features of the image. The V component is enhanced according to the Retinex principle. Finally, the H and the enhanced S and V components are recombined and reflected into RGB space to achieve the purpose of image enhancement. The experimental results showed that the algorithm is superior to the traditional SSR, MSR and MSRCR algorithms in terms of entropy, average gradient and sharpness, and outperforms those based on Gauss function estimation.

FPGA-Based Hardware Architecture for Fuzzy Homomorphic Enhancement Based on Partial Differential Equations

This paper presents an efficient, high speed hardware architecture for a PDE-based variational fuzzy homomorphic color image enhancement algorithm for both RGB and HSI/HSV color space variants. The developed architecture avoids the use of filters either in the spatial or frequency domain, utilizing an easily implementable fuzzy membership function with a high boost emphasis filter characteristic. This eliminates multiply-accumulate (MAC) operations, which result in increased usage of hardware resources, especially in smaller FPGA devices where multipliers are costly. The resulting implemented multiplier-less architecture yields a soft filtering effect due to the fuzzy evaluation when compared with the hard and crisp homomorphic filters based in the frequency and spatial domain. Based on the device utilization information obtained from hardware synthesis, the fuzzy homomorphic algorithm is shown to be the least computationally complex and faster algorithm. Additionally, the proposed architecture consumes the least amount of hardware resources on the target FPGA when compared with the spatial domain homomorphic filter. This reduced hardware structural and computational complexity makes the system easily incorporated within a PDE-based framework for improved results, which enables gradual but controlled illumination correction. The results show that the proposed system is a more effective and versatile enhancement system than those found in the literature.

MODIFIED ALPHA-ROOTING COLOR IMAGE ENHANCEMENT METHOD ON THE TWO-SIDE 2-DQUATERNION DISCRETE FOURIER TRANSFORM AND THE 2-DDISCRETE 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.

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.

MODIFIED ALPHA-ROOTING COLOR IMAGE ENHANCEMENT METHOD ON THE TWO-SIDE 2-DQUATERNION DISCRETE FOURIER TRANSFORM AND THE 2-DDISCRETE 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.

A color image enhancement algorithm based on quaternion representation of vector rotation

Detail enhancement of color images is required in many applications. Unsharp masking (UM) is the most classical tool for detail enhancement. Many generalizing UM approaches have been proposed, for example, the rational UM technique, the cubic unsharp technique, the adaptive UM technique and so on. For color images, these algorithms have three steps: (a) Implement the color2gray step; (b) design an extracting method of high frequency information (HFI) based on the luminance component (LC); (c) complete the enhancing process utilizing the HFI. However, using only the HFI of the LC may lose the HFI of the chrominance component (CC). This paper proposes a quaternion based detail enhancement algorithm to extract details of the color image using both of the luminance and CCs. The proposed algorithm is designed to address three tasks: (1) designing an extraction method of the color high frequency information (CHFI) based on quaternion description of the 3D vector rotation; (2) performing an effective fusion strategy of the CHFI and the gray high frequency information (GHFI); (3) designing a quaternion based measure method of the local dynamic range, based on which the enhancement coefficients of the proposed algorithm can be determined. The performance of the proposed algorithm compares favorably with many other similar enhancement algorithms. The eight parameters can be adjusted to control the sharpness to produce the desired results, which makes the proposed algorithm practically useful.

Study on the Improvement of Color Image Enhancement Algorithm for Robot Vision System

Under the influence of external dynamic lighting or outdoors strong light, image color have a great influence on rapid and accurate positioning of the robot, which requires the robot vision system to obtain the high quality of color image. This paper uses the image pixel conversion ratio algorithm to improve the image recognition algorithm of robot vision system, and the use of multi threshold segmentation technology carries out restructure for image, it can get the high resolution image enhancement effect. In order to realize the algorithm, the form of using VB programming carries out algorithm implementation, and then the image enhancement can calculate the enhanced results table of image color curve distribution and different segmentation threshold image resolution. From the simulation results, this algorithm can enhance the image color effect, which provides technical support for the development and design of robot vision system.

A fast color image enhancement algorithm based on Max Intensity Channel

In this paper, we extend image enhancement techniques based on the retinex theory imitating human visual perception of scenes containing high illumination variations. This extension achieves simultaneous dynamic range modification, color consistency, and lightness rendition without multi-scale Gaussian filtering which has a certain halo effect. The reflection component is analyzed based on the illumination and reflection imaging model. A new prior named Max Intensity Channel (MIC) is implemented assuming that the reflections of some points in the scene are very high in at least one color channel. Using this prior, the illumination of the scene is obtained directly by performing a gray-scale closing operation and a fast cross-bilateral filtering on the MIC of the input color image. Consequently, the reflection component of each RGB color channel can be determined from the illumination and reflection imaging model. The proposed algorithm estimates the illumination component which is relatively smooth and maintains the edge details in different regions. A satisfactory color rendition is achieved for a class of images that do not satisfy the gray-world assumption implicit to the theoretical foundation of the retinex. Experiments are carried out to compare the new method with several spatial and transform domain methods. Our results indicate that the new method is superior in enhancement applications, improves computation speed, and performs well for images with high illumination variations than other methods. Further comparisons of images from National Aeronautics and Space Administration and a wearable camera eButton have shown a high performance of the new method with better color restoration and preservation of image details.

Termite Retinex: a new implementation based on a colony of intelligent agents

The original presentation of Retinex, a spatial color correction and image enhancement algorithm modeling the human vision system, as proposed by Land and McCann in 1964, uses paths to explore the image in search of a local reference white point. The interesting results of this algorithm have led to the development of many versions of Retinex. They follow the same principle but differ in the way they explore the image, with, for example, random paths, random samples, convolution masks, and variational formulations. We propose an alternative way to explore local properties of Retinex, replacing random paths by traces of a specialized swarm of termites. In presenting the spatial characteristics of the proposed method, we discuss differences in path exploration with other Retinex implementations. Experiments, results, and comparisons are presented to test the efficacy of the proposed Retinex implementation.

A Research of Image Difference Algorithm Based on the Correlation of Color Attributes

The CIE color difference formula was developed using simple color patches in controlled viewing conditions. They are not suitable for predicting color differences of spatially complex images. In order to quantify color differences between the original and reproduced images, we present an image difference algorithm based on image contents. This formula uses the Contrast Sensitivity Functions (CSF) to reduce the spatial complexity. The CSF serves to remove information that is imperceptible to the human visual system. This algorithm determines the color attribute weights by means of the correlation analysis between image pairs. Psychophysical experiments confirmed that the metric correlates strongly with the human perception. The proposed algorithm can be used to assess the performance of color image compressions, color image enhancement algorithms, gamut mapping algorithms and so on.

A new framework for retinex-based colour image enhancement using particle swarm optimisation

A new approach for tuning the parameters of MultiScale Retinex (MSR) based color image enhancement algorithm using a popular optimization method, namely, Particle Swarm Optimization (PSO) is presented in this paper. The image enhancement using MSR scheme heavily depends on parameters such as Gaussian surround space constant, number of scales, gain and offset etc. Selection of these parameters, empirically and its application to MSR scheme to produce inevitable results are the major blemishes. The method presented here results in huge savings of computation time as well as improvement in the visual quality of an image, since the PSO exploited maximizes the MSR parameters. The objective of PSO is to validate the visual quality of the enhanced image iteratively using an effective objective criterion based on entropy and edge information of an image. The PSO method of parameter optimization of MSR scheme achieves a very good quality of reconstructed images, far better than that possible with the other existing methods. Finally, the quality of the enhanced color images obtained by the proposed method are evaluated using novel metric, namely, Wavelet Energy (WE). The experimental results presented show that color images enhanced using the proposed scheme are clearer, more vivid and efficient.