Image Deblurring

In digital image processing, image deblurring is a fundamental and crucial technique that employs various algorithms and methodologies to remove noise from images, thereby enhancing image quality and clarity. This process involves filtering out the noisy components of the image signal while preserving as much detail and edge information as possible. Common implementation approaches include spatial domain filters (such as Gaussian blur, median filtering) and frequency domain techniques (like Fourier transform-based filtering). Key algorithms often involve convolution operations with carefully designed kernels and optimization methods for noise reduction. This technology finds extensive applications across multiple domains including medical image processing, non-destructive testing, video processing, and computer vision systems. Modern implementations frequently utilize machine learning approaches, particularly convolutional neural networks (CNNs) trained on noisy-clean image pairs for superior denoising performance.