Abstract
Global image enhancement techniques are used to enhance contrast in images but these techniques are found to be under-enhanced or over-enhanced in differently illuminated regions of the image. Local color correction methods work on local pixel regions to optimize the color contrast enhancement but they also have been found to show a lag while covering pixel regions which are overexposed, compared to those which are underexposed causing local artifacts. In this work, we overcome the shortcomings of both the local color correction and global color correction. This method uses local color correction in the Hue Saturation Luminance (HSL) domain, and fuzzy intensification operators are used to control the color fidelity of the local color corrected images. Thus, is able to sort out the problem of overexposed and underexposed regions and provide optimized contrast enhancement in colored images. Several experiments have been performed to analyze the performance of the proposed method and feasibility as compared to existing techniques. Performance parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM) and Naturalness Image Quality Evaluator (NIQE) is evaluated and the comparison with some existing techniques of contrast enhancement of color images is performed. The obtained result have good contrast and approve the better performance of the proposed method in support of the quantitative measure of perceptual appearance of the processed images and low computational time.
Highlights
Image enhancement is one of the most important steps in any image processing algorithm whether it is pattern detection, image classification or biometric recognition
We find that contrast stretching loses some of the detail information of the images during enhancement, Histogram Equalization and its variations gives better results but it cannot preserve the brightness of the original image
The archive consists of 30 images, which consists of collected images and other popular images used by the other experts[31]
Summary
Image enhancement is one of the most important steps in any image processing algorithm whether it is pattern detection, image classification or biometric recognition. Most of the computer vision based algorithms consist of image quality enhancement steps at some stages. Several methods of image enhancement exist which can improve the characteristics of the images to acceptable level even if they have been acquired with low quality cameras. Different mathematical arbitrations can be applied on the images to improve their characteristics. Even from the perspective of humans, the perceptibility of images is dependent on the contrast of the images. Contrast generally refers to the difference between different pixel levels based on their intensity. The sensitivity to human contrast depends on the spatial frequency; when determining the contrast the spatial content of the picture should be considered. The variation in color and brightness of the element and other items within the same field of view is calculated to provide the visual perception of the real world
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