Abstract
This study introduces a modular framework for spatial image processing, integrating grayscale quantization, color and brightness enhancement, image sharpening, bidirectional transformation pipelines, and geometric feature extraction. A stepwise intensity transformation quantizes grayscale images into eight discrete levels, producing a posterization effect that simplifies representation while preserving structural detail. Color enhancement is achieved via histogram equalization in both RGB and YCrCb color spaces, with the latter improving contrast while maintaining chrominance fidelity. Brightness adjustment is implemented through HSV value-channel manipulation, and image sharpening is performed using a 3 * 3 convolution kernel to enhance high-frequency details. A bidirectional transformation pipeline that integrates unsharp masking, gamma correction, and noise amplification achieved accuracy levels of 76.10% and 74.80% for the forward and reverse processes, respectively. Geometric feature extraction employed Canny edge detection, Hough-based line estimation (e.g., 51.50° for billiard cue alignment), Harris corner detection, and morphological window localization. Cue isolation further yielded 81.87\% similarity against ground truth images. Experimental evaluation across diverse datasets demonstrates robust and deterministic performance, highlighting its potential for real-time image analysis and computer vision.
Abstract (translated)
这项研究介绍了一个用于空间图像处理的模块化框架，集成了灰度量化、颜色和亮度增强、图像锐化、双向转换管道以及几何特征提取。该框架通过逐步强度变换将灰度图像量化为八个离散级别，产生一种海报化效果，在简化表示的同时保持结构细节。颜色增强是通过对RGB和YCrCb色彩空间的直方图均衡实现的，后者提高了对比度并保留了色度保真度。亮度调整是通过HSV值通道操作来实施的，并且图像锐化采用3x3卷积核进行高频细节的增强。 双向转换管道整合了不清晰掩模、伽马校正和噪声放大技术，在前向和反向过程中分别达到了76.10%和74.80%的精度水平。几何特征提取使用Canny边缘检测、基于霍夫变换的直线估计（例如，台球杆对齐角度为51.50°）、Harris角点检测以及形态学窗口定位。 台球杆隔离进一步与地面真实图像相比达到了81.87%的相似度。跨多种数据集进行实验评估证明了其稳健和确定性的性能表现，突显出其在实时图像分析和计算机视觉领域的潜在应用价值。
URL
https://arxiv.org/abs/2510.08449
