22-05-2013, 12:24 PM
An Advanced Enhancement Approach for High Contrast Images of Digital Cameras
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Abstract
This article introduces a new image Enhancement approach suitable for digital cameras. High contrast images are common in the scenes with dark shadows and bright light sources. It is difficult to show the details in both dark and light areas simultaneous on most display devices. For solving this problem, there are many methods of image enhancement proposed to improve the quality of the images. However, most of them often get poor results if the images are high contrast and have wide dynamic range. This method for enhancing the high-contrast digital camera images, which enhances the global brightness and contrast of images while preserving details. It is based on a two-scale decomposition of the image into a base layer, encoding large-scale variations, and a detail layer. The base layer is obtained using an edge preserving filter that is a weighted average of the local neighborhood samples, where the weights are computed based on temporal and radiometric distances between the center sample and the neighboring samples. Only the base layer image is enhanced automatically by using histogram equalization method, thereby preserving detail. The experimental results show the proposed method provides a significant enhancement for the high-contrast images and requires no parameter setting.
INTRODUCTION
Contrast of an image is determined by its dynamic range, which is defined as the ratio between the brightest and the darkest pixel intensities. Contrast enhancement techniques have various application areas for enhancing visual quality of low contrast images. Histogram equalization (HE) is a very popular technique for enhancing the contrast of an image [1]. Its basic idea lies on mapping the gray levels based on the probability distribution of the input gray levels. It flattens and stretches the dynamic range of the image's histogram, resulting in overall contrast improvement. HE has been applied in various fields such as medical image processing and radar image processing [2]. In theory, it can be shown that the mean brightness of the histogram-equalized image is always the middle gray level regardless of the input mean. When brightness preservation is important and necessary, this property is not a desirable one in certain applications.
Methodology
Image Enhancement
Image enhancement is a process principally focuses on processing an image in such a way that the processed image is more suitable than the original one for the specific application. The word ―specific‖ has significance. It gives a clue that the results of such an operation are highly application dependent. The technique falls in two categories on the basis of the domain they are applied on. These are the frequency and spatial domains. The frequency domain methods works with the Fourier Transforms of the image. The term spatial domain refers to the whole of pixels of which an image is composed of. Spatial domain methods are procedures that operate directly on the pixels. A number of enhancement techniques exist in the spatial domain. Among these are histogram processing, enhancement using arithmetic, and logical operations and filters.
Image enhancement operation improves the qualities of an image. They can be used to improve an image‟s contrast and brightness characteristics, reduce its noise content or sharpen its details. In view of the wide usage of loosely defined terms covering the general topic of image-enhancement, it is appropriate to give a precise definition of what this term denotes within the present context. Other terms such as image-processing are often used as synonyms, along with those such as image-restoration and image-manipulation, and catch-all phrases such as photo-editing are now widely used in the an ever-growing modern circle of consumer digital-imaging. But all these and other common terms are frequently used interchangeably, and mean quite different things in different contexts. For the present purposes we define image-enhancement, in the sense used here, with the help of figure 1.
Proposed Algorithm
The contributions of the paper for enhancing the high-contrast digital photos automatically, which enhances the overall brightness and contrast of images while preserving detail. It is based on a separate the colors of the image by decomposing the image into the color image and the intensity image, two-scale decomposition of the image into a base layer, encoding coarse or large-scale image, and a detail layer. The base layer is obtained using an edge preserving filer. This filter is merely a weighted average of the local neighborhood samples, where the weights are computed based on temporal and radiometric distances between the center sample and the neighboring samples. In this paper, methods of image enhancement based on wavelet transform were proposed. However, we cannot obtain more high-frequency information only through multi-scale wavelet transform.
Histogram Equalization
For adjusting the image contrast and brightness, we propose to use histogram equalization to obtain the optimal coarse image. It has the general tendency of spreading the histogram of the input image so that the levels of the histogram-equalized image span a fuller range of the gray scale. The histogram distribution of the image before and after the histogram equalization is shown in Figure 7 and also shows the result of the base layer image enhanced by the histogram equalization. One of the useful advantages of histogram equalization is that it is fully automatic. It is obvious that the dark area is lightened.
Conclusion
In practice, the automatic enhancement function in the commercial image editing software such as Adobe Photoshop or UleadPhotoimpact obtain poor results for the photos with high contrast or high dynamic range. In this paper, we present an image enhancement algorithm based on the weighted filter, histogram equalization and wavelet transformation to solve this problem. The experimental results show that the proposed approach can enhance the high-contrast images effectively; it not only improves the global brightness and contrast of images but also preserves details and remove noise. The other advantage of the proposed method is that it is fully automatic and requires no parameter settings. Therefore, it is useful and suitable for most digital camera users.