Super-Resolution Restoration of Blurry Images
Super-resolution restoration of blurry images - this process generates clearer results than simple interpolation methods by reconstructing high-frequency details through advanced algorithms.
Motion Blur Image Generation and Inverse Filtering
Generation of motion-blurred images with comparative analysis of restoration effects using inverse filtering and Wiener filtering
Maximum Entropy Direct Iterative Method for Image Restoration
A Comparative Study of Maximum Entropy Direct Iterative Method, Wiener Filtering, and Blind Restoration for Recovering Images Blurred by Horizontal Uniform Linear Motion
Assessing Image Sharpness by Calculating Power Spectrum Magnitude
This algorithm evaluates image sharpness by computing the power spectrum magnitude, where sharper images exhibit larger power spectrum values compared to blurred images. Implementation typically involves applying 2D Fourier transform and analyzing frequency domain energy distribution.
Target Extraction for Blurry Images Using Level Set Segmentation
A valuable MATLAB implementation of level set segmentation algorithms specifically designed for object extraction in blurred images, featuring adaptive parameter tuning and noise-resistant processing capabilities
Blur Parameter Estimation of Resultant Blurred Images Using Cepstrum Analysis
Applying blur to images and implementing blur parameter estimation for the resulting blurred images through cepstrum analysis methodology
Image Enhancement Techniques
Transforming blurry images with indistinct details into clearer versions through a series of digital processing techniques, enabling enhanced visibility of image details