Blur-Image

Overview

Blur-Image is a compact image processing toolkit developed in Python, designed to improve image sharpness through advanced blurring techniques. Based on the Richardson-Lucy deconvolution algorithm, this toolkit allows users to apply different blur kernels and iterative enhancements to improve image quality.

Features

Blurring and sharpening: Implement Richardson-Lucy deconvolution for image blurring, as well as standard blurring techniques using different kernels.
Batch image processing: Process entire directories of images for bulk blurring and sharpening.
Quality measurements: Calculate and report the Peak Signal-to-Noise Ratio (PSNR) to measure the quality of processed images.
Visual logging: Color-coded console for easy monitoring of processing steps.

Installation

Clone the repository to your local machine:

git clone https://github.com/mathusanm6/Blur-Image.git
cd Blur-Image

Prerequisites

Ensure you have Python installed along with the following packages:

numpy
scipy
pillow (PIL)

You can install the required packages via pip:

pip install numpy scipy pillow

Usage

After tuning the parameters in the core.py and blind_core.py files, you can run the toolkit using the following command:

python ./run.sh

Richardson-Lucy Deconvolution

Below are some examples of images processed by the Blur-Image toolkit, showing the original images, the blurred versions, and the deblurred outputs after applying various kernels and iteration counts.

Process

Blurring are done using the following kernels:

Average 3x3
Average 5x5
Average 11x11
Gaussian 3x3, sigma: 1.0
Gaussian 3x3, sigma: 2.0
Gaussian 5x5, sigma: 1.0
Gaussian 5x5, sigma: 2.0

Sharpening are done knowing the kernel used for blurring and the number of iterations using the Richardson-Lucy deconvolution algorithm as follows:

Algorithm: Richardson-Lucy Deconvolution

Input:
    blurry_image: the blurred image to be deconvolved, which can be a single channel from a color image or a
    grayscale image.
    psf: the point spread function assumed to have caused the blur.
    iterations: the number of iterations for the algorithm.

Output:
    deblurred_image: the image after deconvolution.

Procedure:
1. Initialize:
    estimate = copy of blurry_image // Starting point for the estimation of the deblurred image.

2. Iterate from 1 to iterations:
    a. Convolve the estimate with the PSF
       convolved_estimate = convolve2d(estimate, psf)

    b. Compute the ratio of the blurry_image to the convolved_estimate
       ratio = blurry_image / (convolved_estimate + small_value) // small_value prevents division by zero.

    c. Convolve this ratio with the mirrored PSF (flip the PSF vertically and horizontally)
       error_estimate = convolve2d(ratio, flip(psf, vertically and horizontally))

    d. Update the estimate by multiplying it with the error_estimate
       estimate = estimate * error_estimate

3. Return the final estimate after all iterations as the deblurred_image.

Original Image

(1) Grayscale Flower

(2) Tiger

Processed Images

(1) Grayscale Flower

Average 3x3	Average 5x5	Average 11x11	Gaussian 3x3, sigma: 1.0	Gaussian 3x3, sigma: 2.0	Gaussian 5x5, sigma: 1.0	Gaussian 5x5, sigma: 2.0
Blurred Images

Unblurred Images
5 Iterations	5 Iterations	5 Iterations	5 Iterations	5 Iterations	5 Iterations	5 Iterations

10 Iterations	10 Iterations	10 Iterations	10 Iterations	10 Iterations	10 Iterations	10 Iterations

15 Iterations	15 Iterations	15 Iterations	15 Iterations	15 Iterations	15 Iterations	15 Iterations

(2) Tiger

Average 3x3	Average 5x5	Average 11x11	Gaussian 3x3, sigma: 1.0	Gaussian 3x3, sigma: 2.0	Gaussian 5x5, sigma: 1.0	Gaussian 5x5, sigma: 2.0
Blurred Images

Unblurred Images
5 Iterations	5 Iterations	5 Iterations	5 Iterations	5 Iterations	5 Iterations	5 Iterations

10 Iterations	10 Iterations	10 Iterations	10 Iterations	10 Iterations	10 Iterations	10 Iterations

15 Iterations	15 Iterations	15 Iterations	15 Iterations	15 Iterations	15 Iterations	15 Iterations

Blind Richardson-Lucy Deconvolution

Process

Sharpening are done not knowing the kernel used for blurring and the number of iterations using the Blind Richardson-Lucy deconvolution algorithm as follows:

Algorithm: Blind Richardson-Lucy Deconvolution

Input:
    blurry_image: the blurred image to be deconvolved.
    initial_psf: initial guess for the point spread function (PSF).
    iterations: number of iterations for the deconvolution process.
    psf_iterations: number of iterations for refining the PSF.

Output:
    deblurred_image: the image after deconvolution.

Procedure:
1. Initialize:
    estimate = copy of blurry_image // Starting estimate for the deblurred image.
    psf = initial_psf // Starting estimate for the PSF.

2. For each deconvolution iteration:
    a. Convolve the estimate with the current psf
       convolved_estimate = convolve2d(estimate, psf)

    b. Compute the ratio of the blurry_image to the convolved_estimate
       ratio = blurry_image / (convolved_estimate + small_value) // small_value prevents 
       division by zero.

    c. Convolve the ratio with the mirrored PSF
       error_estimate = convolve2d(ratio, flip(psf, vertically and horizontally))

    d. Update the estimate by multiplying it with the error_estimate
       estimate = estimate * error_estimate

    e. Update the PSF for a number of p-iterations:
       i. For each PSF iteration:
           A. Convolve the estimate with the current psf
              estimated_convolution = convolve2d(estimate, psf)

           B. Compute the ratio of the original blurry_image to the estimated_convolution
              error_ratio = blurry_image / (estimated_convolution + small_value)

           C. Convolve the error_ratio with the flipped estimate
              full_psf_update = convolve2d(error_ratio, flip(estimate, 
              vertically and horizontally))

           D. Crop the full_psf_update to match the PSF size and update the PSF
              psf_update = crop_center(full_psf_update, size of psf)
              psf = psf * psf_update
              psf = normalize(psf) // Ensures energy of the PSF is preserved.

3. Return the final estimate after all iterations as the deblurred_image.

Original Image

(A) Blurred tiger (Unknown Kernel)

Processed Images

(A) Blurred Tiger (Unknown Kernel)

Average 3x3	Average 5x5	Gaussian 5x5, sigma: 1.0	Gaussian 5x5, sigma: 2.0

15 Iterations, 3 PSF Iterations	15 Iterations, 3 PSF Iterations	15 Iterations, 3 PSF Iterations	15 Iterations, 3 PSF Iterations

30 Iterations, 3 PSF Iterations	30 Iterations, 3 PSF Iterations	30 Iterations, 3 PSF Iterations	30 Iterations, 3 PSF Iterations

60 Iterations, 3 PSF Iterations	60 Iterations, 3 PSF Iterations	60 Iterations, 3 PSF Iterations	60 Iterations, 3 PSF Iterations

120 Iterations, 3 PSF Iterations	120 Iterations, 3 PSF Iterations	120 Iterations, 3 PSF Iterations	120 Iterations, 3 PSF Iterations