TL;DR
A model-based deep denoising framework that integrates three tightly coupled components, and proves that every limit point of the L-ADMM sequence is a stationary point of the nonconvex objective, and that the iterate sequence itself converges globally.
Experimental results on both grayscale and color image restoration demonstrate that the proposed method consistently outperforms representative model-based approaches while achieving performance comparable to state-of-the-art DEQ models based on implicit regularization, despite requiring substantially fewer trainable p...
GSDSplit+ is introduced, a splitting method built upon PnPSplit+, in which the generic denoising block is replaced by a relaxed Gradient Step Denoiser, which retains explicit Poisson fidelity updates.
This paper proposes an ADMM-inspired unrolled plug-and-play solver for Poisson inverse problems that decouples a closed-form data-consistency update from a parameter-efficient prior, implemented as a lightweight decoder operating on frozen CLIP RN50 dense multi-scale features.
This paper proposes a novel learned spherical alternating direction method of multipliers (LSADMMs) for effective Rician noise removal under spherical constraints, and incorporates a noise-level estimation prefix that provides adaptive guidance across noise levels.
Abstract. We describe a practical framework for data-driven regularization in image reconstruction. It combines model expressivity with the guarantees of variational methods. The approach is based on a weakly convex ridge regularizer, defined as the composition of a convolutional filter bank and pointwise potentials c...
This paper proposes a new unsupervised image denoising method based on an improved TV constraint in PINNs that significantly mitigates staircase effects and shows superior denoising robustness, structural fidelity, and computational efficiency in complex noise environments.
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