A high-performance image processing library written in Go.
This project provides reusable image processing algorithms and utilities for computer vision, scientific computing, graphics, and image analysis applications. It focuses on building efficient, modular, and production-ready image processing components from scratch.
- Image Format Support
- Color Space Processing
- Image Enhancement
- Noise Reduction
- Morphological Operations
- Frequency Domain Processing
- Image Segmentation
- Feature Descriptors
- Image Analysis
- Computational Photography
- Performance Optimization
Objective: Build reliable image input and output capabilities.
- PNG
- JPEG
- BMP
- TIFF
- GIF
- WebP
Objective: Implement color space conversions and color manipulation utilities.
- RGB
- RGBA
- Grayscale
- HSV
- HSL
- LAB
- XYZ
- YCbCr
- CMYK
Objective: Improve image quality through enhancement techniques.
- Brightness Adjustment
- Contrast Adjustment
- Gamma Correction
- Histogram Equalization
- CLAHE
- White Balance
- Color Correction
Objective: Reduce image noise while preserving important details.
- Box Blur
- Gaussian Blur
- Median Blur
- Bilateral Filter
- Non-Local Means Denoising
Objective: Implement frequency-based image processing algorithms.
- Fast Fourier Transform (FFT)
- Discrete Fourier Transform (DFT)
- Inverse DFT
- High-pass Filtering
- Low-pass Filtering
- Band-pass Filtering
- Frequency Spectrum Visualization
Objective: Implement binary and grayscale morphology algorithms.
- Erosion
- Dilation
- Opening
- Closing
- Morphological Gradient
- Top Hat
- Black Hat
- Skeletonization
Objective: Partition images into meaningful regions.
- Watershed Segmentation
- Region Growing
- GrabCut
- K-Means Segmentation
- Superpixel Generation
Objective: Implement reusable feature extraction algorithms.
- Histogram of Oriented Gradients (HOG)
- Local Binary Patterns (LBP)
- ORB
- BRISK
- AKAZE
- SIFT
Objective: Extract statistical and structural information from images.
- Histogram Statistics
- Color Statistics
- Texture Analysis
- Image Moments
- Hu Moments
- Image Similarity Metrics
Objective: Build advanced image processing techniques.
- HDR Imaging
- Panorama Stitching
- Exposure Fusion
- Focus Stacking
- Image Blending
Objective: Improve performance and scalability.
- Parallel Processing
- SIMD Optimizations
- Memory Pooling
- Benchmark Suite
- Performance Profiling
Objective: Deliver the first production-ready release.
- Stable Public API
- Comprehensive Documentation
- Example Applications
- Unit Testing
- Benchmark Results
- Release Notes
go-image-processing/
├── benchmarks/
├── cmd/
├── docs/
├── examples/
├── internal/
├── pkg/
│ ├── analysis/
│ ├── color/
│ ├── enhancement/
│ ├── filtering/
│ ├── frequency/
│ ├── imageio/
│ ├── morphology/
│ ├── photography/
│ ├── segmentation/
│ └── descriptors/
├── testdata/
└── LICENSE
- Pure Go implementation
- Minimal external dependencies
- Modular architecture
- Cross-platform compatibility
- High-performance algorithms
- Production-ready APIs
- Well-documented codebase
- Extensive testing and benchmarking
- Educational reference implementation
- Brightness
- Contrast
- Gamma Correction
- CLAHE
- White Balance
- Gaussian Blur
- Median Blur
- Bilateral Filter
- Box Filter
- Erosion
- Dilation
- Opening
- Closing
- Watershed
- GrabCut
- Region Growing
- K-Means
- HOG
- LBP
- ORB
- BRISK
- AKAZE
- SIFT
- FFT
- DFT
- IDFT
- Frequency Filters
- HDR
- Panorama
- Exposure Fusion
- Focus Stacking
Contributions are welcome.
You can contribute by:
- Reporting bugs
- Requesting new features
- Improving documentation
- Optimizing existing algorithms
- Adding new image processing techniques
- Writing tests and benchmarks
Please open an issue before submitting large changes.
This project is licensed under the MIT License.