提出分层空间算法，实现高分辨率图像的量化与特征提取。
Hierarchical Spatial Algorithms for High-Resolution Image Quantization and Feature Extraction
- 分步强度变换将灰度图量化为8级，保留结构细节。
- 双向变换管道正反向准确率分别达76.10%和74.80%。
- 适用于实时图像分析与计算机视觉任务。
本研究提出一种模块化空间图像处理框架，整合灰度量化、色彩与亮度增强、图像锐化、双向变换流水线及几何特征提取。通过分步强度变换，将灰度图像量化为8个离散等级，产生海报化效果，简化表示同时保留结构细节。色彩增强采用RGB与YCrCb空间的直方图均衡化，后者在提升对比度的同时保持色度保真。亮度调节通过HSV值通道实现，锐化使用3×3卷积核增强高频细节。双向变换流水线融合未模糊掩膜、伽马校正与噪声放大，正向与反向过程准确率分别为76.10%与74.80%。几何特征提取采用Canny边缘检测、基于Hough的直线估计（如台球杆对齐角51.50°）、Harris角点检测及形态学窗口定位。杆体分离结果与真实图像相似度达81.87%。跨多个数据集的实验评估表明该方法具有鲁棒且确定性表现，具备实时图像分析与计算机视觉应用潜力。
原文摘要 · Abstract (English)
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.
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