Adaptive Aggregated Histogram Equalization for Color Image Enhancement without Gamut Problem

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

Histogram equalization (HE) is one of the simplest and most effective techniques for enhancing gray-level images. For color images, HE becomes a more difficult task, due to the vectorial nature of the data. We propose a new method for color image enhancement that uses two hierarchical levels of HE: global and local. In order to preserve the hue, equalization is only applied to intensities. For each pixel (called the ‘‘seed’’ when being processed) a variable-sized, variable-shaped neighborhood is determined to contain pixels that are ‘‘similar’’ to the seed. Then, the histogram of the region is stretched to a range that is computed with respect to the statistical parameters of the region (mean and variance) and to the global HE function (of intensities), and only the seed pixel is given a new intensity value. We applied the proposed color HE method to various images and observed the results to be subjectively ‘‘pleasant to the human eye,’’ with emphasized details, preserved colors, and with the histogram of intensities close to the ideal uniform one. The results compared favorably with those of three other methods (histogram explosion, histogram decimation, and three-dimensional histogram equalization) in terms of subjective visual quality.

In this paper, a new method for color image enhancement based on wavelet transform is presented. The three different colour channels such as R, G, and B of the input color image are separated initially. The proposed technique decomposes the different colour channels into the four frequency sub-bands by using Discrete Wavelet Transform (DWT). Histogram Equalization is applied to the low frequency sub-band only. Finally, the inverse wavelet transform is applied to reconstruct the enhanced image. Mean is the parameter which is used to measure the performance of the proposed method.

The first step in many techniques for processing intensity and saturation in color images keeping hue unaltered is the transformation of the image data from RGB space to other color spaces such as LHS, HSI, YIQ, HSV, etc. Transforming from one space to another and processing in these spaces usually generate a gamut problem, i.e., the values of the variables may not be in their respective intervals. We study enhancement techniques for color images theoretically in a generalized setup. A principle is suggested to make the transformations gamut-problem free. Using the same principle, a class of hue-preserving, contrast-enhancing transformations is proposed; they generalize existing grey scale contrast intensification techniques to color images. These transformations are also seen to bypass the above mentioned color coordinate transformations for image enhancement. The developed principle is used to generalize the histogram equalization scheme for grey scale images to color images.

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.

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.

A novel method for color image enhancement is proposed as an extension of the scalar-diffusion-shock-filter coupling model, where noisy and blurred images are denoised and sharpened. The proposed model is based on using the single vectors of the gradient magnitude and the second derivatives as a manner to relate different color components of the image. This model can be viewed as a generalization of the Bettahar-Stambouli filter to multivalued images. The proposed algorithm is more efficient than the mentioned filter and some previous works at color images denoising and deblurring without creating false colors.

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.

Color image modification is an essential component for several applications and the grayscale transformation is generally mapped to the color image indirectly. Although several techniques have been used for this transfer, they suffer from gamut mapping issue. In this paper, it is aimed to study the mapping of grayscale transformations to the color scale in different perspectives. Modifying the image in different color space than the original retains hue to a promising extent, but suffers from the gamut problem. A generic scheme to map grayscale changes to the color space for all kinds of spatial modification is proposed here. The hue preserving color image enhancement (HPCE) scheme discussed here is free of gamut-mapping issue and shows promising results in transferring the grayscale transformation to the color image in a simplistic manner. The proposed HPCE scheme is analysed qualitatively through visual appearance and quantitatively using color difference metrics SHAME and CID, gray image difference and EBCM measures. Different gray scale transformations such as S-type enhancement and different forms of histogram equalization techniques are applied on Berkeley dataset of 500 images to prove the efficacy of proposed algorithm.

Histogram equalization is a well known tool for enhancing the contrast and brightness of grayscale images. Grayscale histogram equalization has been extended to color images with limited success. One common method is to equalize the illumination component, while leaving the saturation and hue components unchanged. This method doesn't improve the overall color saturation of the image. Another approach is to apply equalization techniques in the RGB color space. The difficulty in using the RGB color space is that it does not correspond to human interpretation of color. This paper describes a method for histogram equalization of the saturation component using the color difference (or C-Y) color space. Since equalization of the saturation component alone leads to color artifacts, attention is given to the relationship that exists between saturation and intensity.

The limitations of the hardware and dynamic range of digital camera have created the demand for post processing software tool to improve image quality. Image enhancement is a technique that helps to improve finer details of the image. This paper presents a new algorithm for contrast enhancement, where the enhancement rate is controlled by clipped histogram approach, which uses standard intensity deviation. Here standard intensity deviation is used to divide and equalize the image histogram. The equalization processes is applied to sub images independently and combine them into one complete enhanced image. The conventional histogram equalization stretches the dynamic range which leads to a large gap between adjacent pixels that produces over enhancement problem. This drawback is overcome by defining standard intensity deviation value to split and equalize the histogram. The selection of suitable threshold value for clipping and splitting image, provides better enhancement over other methods. The simulation results show that proposed method out performs other conventional histogram equalization (HE) methods and effectively preserves entropy.

A genetic algorithm approach to color image enhancement

Automatic color equalization (ACE) algorithm is an effective method for color image enhancement, but its computational complexity is extremely high. In this paper, we first modify the ACE algorithm to reduce the computational complexity and realization cost while maintaining good visual quality. Subsequently, an efficient VLSI architecture for the hardware-friendly ACE algorithm is proposed to meet the requirement of real-time image enhancement. FPGA (Field Programmable Gate Arrays) implementation result shows that the proposed architecture can operate at 120MHz and achieve a throughput of 60 frame/s for 256×256 resolution images using about 1.15k and 1.78k of FPGA's logic (LUT) and register resources, respectively. Compared with the existing design, the proposed architecture can achieve higher performance with fewer hardware resources and comparable visual quality.

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.

Segmentation of color images is a tricky task. Regaining the segments in an image using the content image is a difficult and significant problem. Medical imaging is the most alive research topic from the past two decades. From the medical diagnosis of patients suffering from various diseases, abnormal regions in the organs can be easily identified, which is a greatest achievement. It is experimentally proved that graph based segmentation methods are better than the other segmentation techniques, especially when combined with statistical methods. We have proposed color image segmentation by adaptive graph cut method in this paper. It consists of two important stages. During the first phase we enhance the input color image using transformation technique as the image may contain noise, may be of low contrast and missing some color statistics. Then this enhanced color image is processed under graph cut technique to get better results, especially for the analysis of medical and general images. The proposed method contributes to medical imaging by means of image segmentation and also to other general image analysis. Our experimental results are found to very good in segmenting color images.