Metadata-Version: 2.4
Name: image_sharpner
Version: 0.1.8
Summary: A lightweight adaptive multi-frequency image sharpening library.
Author-email: Md Istiak Tanvir <eruddro@gmail.com>, Asma Akter <asmaul9377@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/ITR-Ruddro/Lightweight-Image-Sharpening-Method
Keywords: image processing,image enhancement,image sharpening,adaptive sharpening,computer vision,opencv,frequency decomposition
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.19
Requires-Dist: opencv-python>=4.0.0
Dynamic: license-file

Author -
Md Istiak Tanvir (eruddro@gmail.com)
Asma Akter (asmaul9377@gmail.com)


Overview -

image-sharpner is a lightweight and efficient image enhancement library designed to improve edge clarity, restore fine textures, and enhance visual details through an adaptive multi-frequency sharpening technique.
The package supports both grayscale and color images and works seamlessly with popular computer vision pipelines.

Key Features -

1. Adaptive multi-frequency sharpening to enhance details without amplifying noise
2. Preserves natural textures and avoids halo artifacts
3. Works with underwater, low-light, and blurred images
4. Fast, NumPy-based implementation compatible with OpenCV
5. Easy integration into machine learning, deep learning, and image-processing workflows

Why Use This Package?
Traditional sharpening filters often overshoot edges, create ringing artifacts, or boost noise in smooth regions.


Installation -

pip install image-sharpner

Usage Example -

from image_prep  import image_sharpner



#Insert Image path -

sharp = image_sharpner('abc.jpg')





Supported Image Types -
jpg/png/jpeg/webmp

Compatibility -
Python 3.7+
NumPy
OpenCV


License - MIT License
