GPSR-BB Algorithm for 2D Image Reconstruction
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This article explores the application of the GPSR-BB (Gradient Projection for Sparse Reconstruction with Barzilai-Borwein steps) algorithm for reconstructing 2D images, aiming to achieve enhanced clarity and accuracy. As a sparsity-based image reconstruction method, GPSR-BB optimizes pixel values to reduce noise and distortion in images. The algorithm operates by representing an image as a linear combination of basis elements, enabling effective reconstruction and restoration. Key implementation aspects include solving the optimization problem through gradient projection techniques with adaptive step sizes determined by the Barzilai-Borwein method. We will detail the algorithm's underlying principles and practical applications, supplemented with executable examples demonstrating critical functions such as sparse coefficient estimation and iterative optimization processes to help readers thoroughly understand and implement the algorithm.
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