#Abstract
This work explores the implementation of various preprocessing techniques, including Log-Based, Gamma-Based, and Histogram-Based Transformations, for enhancing image quality and preparing data for downstream computer vision tasks. Log transformations are employed to compress the dynamic range of pixel intensities, Gamma transformations adjust brightness and contrast based on nonlinear mappings, and Histogram transformations redistribute pixel intensities to improve image contrast. These preprocessing methods are critical for improving the visibility of image details and optimizing subsequent processing stages. This report details the methodologies and results obtained through these approaches, providing insights into their practical applications and comparative effectiveness.

Introduction

Image preprocessing is a vital step in computer vision pipelines, aimed at improving image quality and extracting relevant features for analysis. This study focuses on three preprocessing methods: Log-Based, Gamma-Based, and Histogram-Based Transformations. Log transformations are widely used for reducing dynamic range, Gamma transformations for adjusting brightness, and Histogram transformations for contrast enhancement. By implementing these methods from scratch, this work provides an understanding of their mathematical principles, practical implementations, and performance comparisons.

Methodology

Log-Based Transformation:

Input images are converted to grayscale and normalized to a range of [0, 1].

Logarithmic scaling is applied using the formula:
, where c is a scaling constant.

The transformed image is rescaled back to the 8-bit range for visualization.

Gamma-Based Transformation:

Gamma correction adjusts image intensity using the formula:
, where gamma<1 brightens the image and gamma>1 darkens it.

The implementation involves inputting Gamma values, normalizing intensities, applying the transformation, and visualizing the results.

Histogram-Based Transformation:

The histogram of grayscale intensities is calculated and normalized to obtain the cumulative distribution function (CDF).

The pixel intensities are redistributed using the CDF to achieve uniform histogram equalization.

The processed image is visualized alongside the original histogram and transformed histogram for comparison.

Experiments:

Preprocessing methods were applied to various grayscale images to evaluate their effectiveness in enhancing image details. Images of diverse intensities and contrasts were selected to demonstrate the transformations' versatility. Gamma values were tested for both brightening and darkening effects, while histogram equalization was applied to images with poor contrast.

Conclusi