Abstract
Addressing the limitations of pre-defined dictionaries in image processing, this study introduces Self-Inspired Bases-based Sparse Encoder (SIBS), a novel approach that dynamically generates image-specific atoms by leveraging existing dictionaries and input data. Extensive numerical analysis and simulations demonstrate that SIBS enhances sparse coding performance in three key areas: improved quality resolution via precise image reconstruction, accelerated convergence speed, and competitive compression performance rivaling techniques like JPEG and JP2000. Experiments show that SIBS enhances quality levels and NMSE at the same number of non-zero coefficients and outperforms atoms learned directly from the image at low error levels. SIBS also effectively improves the performance of various learned and structured dictionaries by serving as auxiliary bases, enhancing quality resolution, convergence rate, and dictionary efficiency. Additionally, SIBS has potential as a competitive image compression method. The study’s discussion on SIBS limitations guides future research, including examining its performance with other greedy pursuit algorithms and reevaluating the encoder with other lossless and filter techniques.
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