$ show BBuf/Image-processing-algorithm
This repository implements a wide range of image processing algorithms in C++ (with some Python components for license plate recognition). It provides functionalities including contrast enhancement, color space conversion, filtering (median, Gaussian, bilateral), feature detection (edges, corners), image interpolation, haze removal, and artistic effects similar to Photoshop. The architecture is modular with algorithms grouped by category in separate directories. Many files include main() functions allowing them to be compiled and run as standalone demonstrations. The primary external dependency is OpenCV, with some files using OpenMP for parallel processing and Intel intrinsics for AVX optimizations. The license plate recognition system integrates C++ preprocessing with TensorFlow/Keras models in Python for character recognition.
The LLM identified 5 files worth highlighting for reuse:
Implements OpenBLAS and OpenMP accelerated 2D convolution for grayscale images. Reformulates convolution as a GEMM (General Matrix Multiply) operation for significant performance gains. Uses bit-shift approximations for efficient RGB-to-grayscale conversion and OpenMP for parallel processing across image rows. Demonstrates non-obvious skill in HPC techniques applied to image processing.
Extends the GEMM-accelerated convolution approach to color (RGB) images using a clever block matrix formulation. Processes each color channel separately through optimized matrix multiplications while maintaining data locality. Shows advanced understanding of both image processing fundamentals and high-performance computing techniques to accelerate standard convolution operations.
Implements a fast median blur algorithm using histogram technique to achieve O(1) time complexity per pixel. Maintains a histogram of pixel values in the sliding window and updates it incrementally as the window moves. Demonstrates non-obvious skill in algorithm optimization by replacing the naive O(window_size) approach with a constant-time histogram-based solution.
Provides a complete implementation of the Harris corner detector from scratch. Includes Sobel gradient computation, Gaussian smoothing via separable filters, Harris response calculation, and non-maximum suppression for corner localization. Shows proper implementation of a classic feature detection algorithm with attention to numerical stability and correctness.
Implements the guided filter algorithm, an edge-preserving smoothing filter that uses a guidance image to determine filtering characteristics. Contains both the mathematical formulation and an optimized O(1) time implementation using box filters. Demonstrates correct implementation of a sophisticated edge-preserving filter useful for tasks like haze removal and detail enhancement.