Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

Abstract

A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.

Ensemble Multifeatured Deep Learning Models and Applications: A Survey

“…Computer vision is a rapidly evolving field that aims to enable machines to interpret and understand still and stereo visual information from the surrounding world [40]. The primary goal of computer vision is to develop algorithms and techniques that can automatically extract meaningful information from images and videos, such as object recognition [41], scene understanding [2], and motion analysis [42].…”