An Algorithm for Vehicle Image Enhancement using Modified Fire Hawk Optimizer and Detection of Number Plates

Abstract

The role of image enhancement is crucial for the processing of images to enhance their quality. Image pre-processing is essential in gathering precise data when detecting any vehicle using its license plate. It requires a method to improve the images to help the persons detect any vehicle and reach the vehicle's owner easily. However most state-of-the-art methods induce different types of distortion such as intensity shift, wash-out, noise and intensity saturation. For this purpose, in this paper, a meta heuristic Modified Fire Hawks optimizer technique has been introduced, which is used to optimize the parameter of Bi-Histogram Equalization with the Adaptive Sigmoid Function method. The pixel and graphic quality of the images are improved with this new technique. Mean square error (MSE), Peak Signal-to-Noise Ratio (PSNR), Mean Absolute Error (MAE), Structural Similarity Index Metric (SSIM), and Absolute Mean Brightness Error (AMBE) are the metrics applied in this paper to check the quality of the processed images. In this paper, main objective of work is to improve the image quality of vehicles and enhancing the image without distortion. This paper is organized in different sections. Section 1 is the introduction section. In section 2, Literature review is presented in which all recent works related to the topic are studied thoroughly. Section 3 describes the methodologies used for the purpose so far and proposed methodology. In section 4, results are shown and compared with the previous results The results of the proposed method are comparatively better than the other state-of-the-art methods for detecting any number of vehicle images. Section 5 briefs the paper and focus on future scope and section 7 describes the various references used for the paper.