Blurry images can be improved using computer vision techniques like deblurring algorithms, which enhance image sharpness by reversing the blurring effect. These algorithms often use methods like deconvolution, Wiener filters, or machine learning-based approaches.
Deep learning models, such as those based on GANs or CNNs, can learn to reconstruct sharp images from blurred inputs by identifying patterns and approximating the missing details. Tools like OpenCV provide libraries for implementing basic deblurring techniques.
While results may vary depending on the severity of the blur and the algorithm used, advancements in computer vision continue to improve the effectiveness of these techniques in restoring image quality.