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Image Processing Scripts

This repository contains various scripts for image processing, including basic Bayer pattern reconstruction, color dithering using Floyd-Steinberg algorithm, and image transformations such as rotation, scaling, and skewing. The code uses Python libraries like NumPy, Matplotlib, scikit-image, and SciPy.

Table of Contents

  1. Basic Bayer Pattern Reconstruction
  2. Color Dithering
  3. Image Transformations

Basic Bayer Pattern Reconstruction

The part1 function performs reconstruction of an RGB image from a grayscale Bayer pattern image using the GRGB pattern. The process includes:

  • Interpolation of the green channel (IG)
  • Reconstruction of the red channel (IR)
  • Reconstruction of the blue channel (IB)

Usage

  1. Place the input image files (PeppersBayerGray.bmp, gridB.bmp, gridR.bmp, gridG.bmp) in the working directory.
  2. Run the script to display the reconstructed RGB image and its channels.
python script_name.py

Color Dithering

This section includes functions for dithering an image using Floyd-Steinberg dithering algorithm and clustering colors with KMeans. The primary functions are:

  • findPalette: Generates a color palette using KMeans clustering.
  • ModifiedFloydSteinbergDitherColor: Applies Floyd-Steinberg dithering using the generated palette.

Image Transformations

The script includes functions for performing image transformations:

  • rotate_image: Rotates the image by a specified angle.
  • scale_image: Scales the image by a specified factor.
  • skew_image: Skews the image by a specified factor.
  • combined_warp: Applies a combination of rotation, scaling, and skewing transformations.
  • combined_warp_bilinear: Performs combined warp with bilinear interpolation.

Requirements

Ensure the following libraries are installed:

  • NumPy
  • Matplotlib
  • scikit-image
  • SciPy
  • scikit-learn
pip install numpy matplotlib scikit-image scipy scikit-learn