Hue Preserving Research Articles

基因表达式编程优化的色调保持低照度图像增强

基因表达式编程优化的色调保持低照度图像增强

Color Image Modification with and without Hue Preservation

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.

Hue preserving‐based approach for underwater colour image enhancement

In this study, a novel underwater colour image enhancement approach based on hue preserving is presented by combining hue-saturation-intensity (HSI) and HS-value (HSV) colour models. In this study, the proposed wavelet-domain filtering (WDF) and constrained histogram stretching (CHS) algorithms are operated on HSI and HSV colour models, respectively. The degraded image is first converted from red-green-blue colour model into the HSI colour model, wherein the hue component H is preserved and WDF algorithm is executed on the S and I components. Similarly, the image is further converted into the HSV colour model, wherein H component is kept invariant as well and CHS algorithm is applied on the S and V components. The authors' key contribution is that the H preserving method can improve image quality in terms of contrast, colour rendition, non-uniform illumination, and denoising. In addition, experimental results show that the proposed approach outperforms several other state-of-the-art algorithms.

Hue preserving colour image enhancement models in RGB colour space without gamut problem

All hue preserving colour image enhancement techniques are associated with the change of colour space, such as RGB to hue saturation intensity (HSI), hue saturation value (HSV), lightness-hue-saturation (LHS), and YUV etc. In these colour spaces intensity or saturation or both have been processed then recombined with unchanged hue component to build a hue preserving method. But all the above techniques are time-consuming for colour space conversion and also introduce gamut problem for which the values of enhanced pixels may not lie within their respective range. In this study, three hue preserving models corresponding to three colour spaces viz. HSI, HSV and YUV, have been proposed by considering only RGB and CMY colour spaces. Any grey level contrast enhancement method can be successfully employed for a colour image through those models. In this paper, a novel variant of Histogram equalisation (HE) has been proposed based on entropy based segmentation to enhance the contrast. The proposed variant called entropy based brightness preserved dynamic histogram equalisation (EBBPDHE) is a modification of brightness preserved dynamic histogram equalisation (BPDHE).

Hue preserving colour image enhancement models in RGB colour space without gamut problem

Hue preserving colour image enhancement models in RGB colour space without gamut problem

Color Image Enhancement Using Optimal Linear Transform with Hue Preserving and Saturation Scaling

Color Image Enhancement Using Optimal Linear Transform with Hue Preserving and Saturation Scaling

Hue Preserving Multi-level Expansion Method Based on Saturation for Wide Gamut Displays

Hue Preserving Multi-level Expansion Method Based on Saturation for Wide Gamut Displays

Vector Ordering and Morphological Operations for Colour Image Processing: Fundamentals and Applications

: The extension of concepts of greyscale morphology to colour image processing requires the use of a proper ordering of vectors (colours) and the definitions of infimum and supremum operators in an appropriate colour space. In this paper, a new approach to colour image morphology is proposed. It is based on a new ordering of vectors in the HSV colour space that is partial ordering. The proposed approach is hue preserving, and it is not a component-wise technique. Its basic characteristic is that it is compatible to the standard greyscale morphology: its fundamental and secondary operations possess the same basic properties as their greyscale counterparts, and furthermore, it is identical to greyscale morphology when it is applied to greyscale images. Examples that illustrate the application of the defined operations to colour images are provided. Moreover, the usefulness of the new method in various colour image processing applications, such as colour image edge detection, object recognition, vector top-hat filtering and skeleton extraction, is demonstrated.

A hue preserving enhancement scheme for a class of colour images

This paper presents a method which enhances the contrast between two objects in a colour image. The method assumes that images are taken from a microscope that looks at a three-dimensional microstructure with a parallel beam of reflected light. Accordingly, the Phong model of illumination is chosen. The image intensity function is shown to contain an additive term to model specular reflection as a function of incident intensity, and a multiplicative factor due to attenuation. Thus a contrast enhancement problem reduces to the estimation and normalisation of these terms.

Color Systems and Color Image Enhancement Methods

There are many color systems. Some systems are correspond to the human visual system, such as the Munsell color system. Other systems are formulated to ease data processing in machines, such as RGB color space. At first, Munsell color system is introduced in this paper. Next, RGB color system and hue-saturation-intensity (HSI) color system which is derived from RGB color systems are reviewed. HSI color system is important, because HSI color system is closely related to Munsell color system. We introduce the advantage and drawbacks of the conventional HSI color space. Furthermore, the improved HSI color system is introduced. The second half of this paper, we introduce a lot of color image enhancement methods based on the histogram equalization or the differential histogram equalization. Since hue preserving is necessary for color image processing, intensity processing methods by using both intensity and saturation in HSI color space are reviewed. Finally, hue preserving color image enhancement methods in RGB color system are explained.