Histogram Equalization for Grayscale Images and Comparison with OpenCV Library
Tayfun Celebi, Ibraheem Abdullah Mohammed Shayea, Ayman A. El‐Saleh, Sawsan Ali, Mardeni Roslee · 2021
The noisy images collected during historical research make it difficult to detail the studies and draw more comprehensive findings. Detailing and updating these images makes it much easier to find information and increases the density of data. Therefore, in this study a histogram equalization method is proposed to reduce the noise in historical images. The method processes the image’s pixel values one by one while also applying the normalization process to keep the density graph steady. In this way, the harmony between density transitions ensures that the quality of the image is higher. The proposed method is compared to the OpenCV algorithm. As a result of this comparison, it is shown that the proposed algorithm is more successful in linearization.