Section Article

The GSR Algorithm for Picture Improvement Deblurring and Noise Reduction
Author(s): Shahrukh Sheikh

Abstract
The continuous evolution of digital imaging technologies has increased the demand for high-quality pictures that are free from noise distortions and motion blur. With the proliferation of smartphones surveillance cameras medical imaging devices and scientific research instruments achieving superior image clarity has become an indispensable requirement across numerous applications. Traditional image enhancement techniques frequently struggle to handle complex degradations caused by motion low-light conditions sensor limitations or environmental interference. In response to these challenges the Gradient Sparse Regularization (GSR) algorithm has emerged as an innovative and efficient computational method for improving images performing deblurring and reducing noise. The GSR algorithm is built on the principle that natural images typically exhibit sparsity in gradient domains—meaning most pixels share smooth intensity transitions except at edges. By exploiting this sparsity the GSR approach reconstructs fine details preserves edges minimizes artifacts and enhances the overall quality of degraded images.