Abstract

Dictionary learning (DicL) is a fundamental technique in sparse representation, widely applied in image processing. As a promising deep extension of traditional DicL, Deep K-SVD (DKSVD) inherits the interpretability of classical models while benefiting from the strong learning capacity of deep networks. However, its reliance on static dictionaries limit adaptability in complex scenarios. To overcome this limitation, we propose DS-DKSVD, a dynamic-static extension of DKSVD, which integrates a hybrid dictionary composed of static and dynamic components. The static component, represented by network parameters, captures global features from training data, while the dynamic component, generated by a dedicated sub-network, adapts to specific input characteristics. During patch averaging, DS-DKSVD dynamically assigns weights, enhancing inter-patch variation handling. Extensive experiments on non-blind and blind image denoising demonstrate its superiority over existing methods. DS-DKSVD achieves up to 0.46 dB and 0.42 dB improvements in PSNR over the original DKSVD and its adaptive variant (AKSVD), respectively. Beyond denoising, a preliminary image classification task highlights the broader applicability of DS-DKSVD. Complementing these quantitative results, visualizations of the learned hybrid dictionary provide qualitative evidence of its interpretability, revealing the complementary roles of static and dynamic components. The source code for the DS-DKSVD is publicly available at https://github.com/yaojingzeo/DS-DKSVD.
 
　

Paper

Z. Sun^#*, J. Yao, C. Zhang. Dynamic–static hybrid dictionary learning: enhancing deep K-SVD for image denoising and beyond. Vis Comput, vol. 42,138, 2026. [pdf | code]

Motivation

Architecture

Visualization Results

Theories