The goal of contrast enhancement is to improve visibility of image details without introducing unrealistic visual appearances and/or unwanted artefacts. While global contrast-enhancement techniques enhance the overall contrast, their dependences on the global content of the image limit their ability to enhance local details. They also result in significant change in image brightness and introduce saturation artefacts. Local enhancement methods, on the other hand, improve image details but can produce block discontinuities, noise amplification and unnatural image modifications. To remedy these shortcomings, this article presents a fusion-based contrast-enhancement technique which integrates information to overcome the limitations of different contrast-enhancement algorithms. The proposed method balances the requirement of local and global contrast enhancements and a faithful representation of the original image appearance, an objective that is difficult to achieve using traditional enhancement methods. Fusion is performed in a multi-resolution fashion using Laplacian pyramid decomposition to account for the multi-channel properties of the human visual system. For this purpose, metrics are defined for contrast, image brightness and saturation. The performance of the proposed method is evaluated using visual assessment and quantitative measures for contrast, luminance and saturation. The results show the efficiency of the method in enhancing details without affecting the colour balance or introducing saturation artefacts and illustrate the usefulness of fusion techniques for image enhancement applications.
The limitations in image acquisition and transmission systems can be remedied by image enhancement. Its principal objective is to improve the visual appearance of the image for improved visual interpretation or to provide better transform representations for subsequent image processing tasks (analysis, detection, segmentation, and recognition). Removing noise and blur, improving contrast to reveal details, coding artefact reduction and luminance adjustment are some examples of image enhancement operations.
Image Fusion-based Contrast Enhancement Pdf Free
Achromatic contrast is a measure of relative variation of the luminance. It is highly correlated to the intensity gradient [1]. There is, however, no universal definition for the contrast. It is well established that human contrast sensitivity is a function of the spatial frequency; therefore, the spatial content of the image should be considered while defining the contrast. Based on this property, the local band-limited contrast is defined by assigning a contrast value to every point in the image and at each frequency band as a function of the local luminance and the local background luminance [2]. Another definition accounts for the directionality of the human visual system (HVS) in defining the contrast [3]. Two definitions of contrast measure for simple patterns have been commonly used. The contrast for periodic patterns, like sinusoidal gratings, is measured using Michelson formula [4]. Weber contrast [2] is used to measure the local contrast of a small target of uniform luminance against a uniform background. However, these measures are not effective for complicated scenarios like actual images with different lightning conditions or shadows [5, 6]. Weber's law-based contrast (used in the case of simple stimuli in a uniform background [7]) led to a metric that was later developed into a suitable measure of contrast (measure of enhancement (EME) or the measure of enhancement by entropy EMEE [8, 9]) for complex images. The Michelson contrast law was later included to improve this measure [10].
Most of the image contrast-enhancement techniques are applied to grayscale images. However, the evolution of photography has increased the interest in colour imaging and consequently in colour contrast-enhancement methods. The goal of colour contrast enhancement in general is to produce appealing image or video with vivid colours and clarity of details intimately related to different attributes of perception and visual sensation. Techniques for colour contrast enhancement are similar to those for grayscale images. Colour imaging may be considered as a channel-by-channel intensity image processing scheme. This is based on the assumption that we can process each of the monochrome channels separately and finally combine the results. HE-based approaches are common for enhancing the contrast in grayscale images. Histogram-based colour enhancement methods have also been proposed in [28, 29]. This is a three-dimensional problem carried out in the RGB space. However, RGB is not a suitable space because of its poor correlation with the HVS. Moreover, independent equalization of RGB leads to a hue shift. Another approach to colour enhancement is to transform the image from the RGB space to other colour spaces such as the CIELAB, LHS, HSI, HVS, etc. However, the useful range of saturation decreases as we move away from medium luminance values. Conversion back to RGB can lead to colour mismatch. HE of the intensity component improves contrast but de-saturates areas in the image. Similarly, the equalization of saturation alone leads to colour artefacts. Therefore, as these methods focus on detail improvement and not on perception of colour enhancement, they may result in colour degradation. Psychologically derived colour enhancement methods are presented in [30, 31]. Both these approaches consider the HVS model where only details and dynamic range are enhanced but colour constancy is also achieved. Jobsen et al. [31] consider a complex HVS model to achieve sharpening, colour constancy and dynamic range compression. These approaches based on retinex theory (such as the single scale retinex--SSR [23] and multi-scale retinex--MSR [32]) aim to improve image rendering close to the original scene and to increase the local contrast in dark regions. However, both SSR and MSR suffer from graying out effect which may appear in large uniform colour areas in the image [33]. Some transform-based contrast-enhancement methods such as the wavelet [34], curvelet [35] and steerable filter [33] transform methods use some characteristics of the HVS to design contrast-enhancement algorithms.
The above discussion indicates that despite many efforts, intensity shift and over-enhancement are still drawbacks of many enhancement methods. Some attempts [36] have been made to design algorithms to integrate local and global information and improve enhancement results. To overcome these limitations, we propose to use image fusion to combine the useful properties and suppress the disadvantages of the various local and global contrast-enhancement techniques, thus improving their performance. Our approach relies on simple image quality attributes like sharpness, details visibility and colour characteristics. Metrics to measure the contrast, and colour characteristics of the gray scale images are defined. The adjustable image measures for contrast and colour are then used to guide the fusion process. A related fusion approach is used in the context of exposure fusion in [37]. We use a similar blending strategy, but employ different quality measures. The proposed method is tested by fusing the output from some well-known image enhancement algorithms like HE [23], contrast-limited adaptive HE (CLAHE) and imadjust function.
Another difficulty in dealing with contrast-enhancement algorithms is the subjective nature of image quality assessment. Subjective enhancement evaluation involves an expert judge to identify the best result among a number of enhanced output images. In general, contrast enhancement is evaluated subjectively in terms of details visibility, sharpness, appearance and noise sensitivity [33]. Good contrast-enhancement algorithms aim to provide local and global contrast improvements, low noise amplification and enhanced images free of saturation, over-enhancement and colour shift problems. Many image quality metrics have been developed for image distortion estimation [38] but there are only a few ad hoc objective measures for image enhancement evaluation [1, 39]. So far, there is no suitable metric for the objective measure of enhancement performance on the basis of which we can sort the enhanced images according to visual quality and detail enhancement. Statistical measures of gray level distribution of local contrast enhancement based on mean, variance or entropy have not been meaningful. A measure based on the contrast histogram shows much greater consistency than statistical measures [40]. Measures for contrast performance based on HVS are proposed in [41]. In this study, we define metrics to measure the contrast enhancement, saturation and luminance/brightness in an effort to define objective metrics to measure the perceptual image quality of the contrast-enhanced images. The proposed method is also used to fuse the output of different tone mapping methods. The performance of the method is evaluated using quantitative measures and subjective perceptual image quality evaluation.
Contrast-enhancement algorithms achieve different amounts of detail preservation. Contrast enhancement can lead to colour shift, washed out appearance and saturation artefacts in regions with high signal activity or textures. Such regions should receive less weight, while the areas with greater details or with low signal activity should receive higher weight during fusion. We define image quality measures which guide the fusion process. These measures are consolidated into a scalar weight map to achieve the fusion goals described above. This section is organized as follows. We first define metrics to measure the contrast and luminance of the enhanced images. Next, the computation of a scalar weight map is explained.
We use first-order derivative to calculate the contrast metric because first-order derivatives have a stronger response to gray level step in an image and are less sensitive to noise. A similar contrast measure based on the local pixel intensity differences was proposed in [42]. Other authors measure the contrast by applying a Laplacian filter (second-order derivative) to the image and taking the absolute value of the filter response [37]. Second-order derivatives have a stronger response to a line than to a step and to a point than to a line [11]. As second-order derivative is much more aggressive than first-order derivative in enhancing sharp changes, it can enhance noise points much more than first-order derivative. There are also some definitions for the local contrast such as the ones defined in [43, 44] which are consistent with the HVS. Here, for the sake of simplicity we use the gradient as a local contrast measure. 2ff7e9595c
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