TL;DR
An adaptive multi-scale decoding framework that effectively balances global context with fine-grained detail is proposed that exhibits superior robustness and generalization across diverse domains, effectively alleviating limitations of existing fusion-based approaches.
High-Compression videos suffer from severe distortions, among which degradation in person regions has the greatest impact on viewers’ immersive experience. Existing quality enhancement techniques usually focus on overall image denoising or super-resolution, often overlooking the crucial recovery of fine structures in t...
The proposed MSHCDI-Net is a Multi-Scale Hybrid Cross-Domain Interaction Network that effectively integrates CNN and Transformer branches to jointly capture local texture details and global contextual relationships and achieves improved structural consistency and detail restoration in low-light scenes.
This work proposes RNSIDNet, a novel forensic framework that achieves robust detection through enhanced RGB-Noise representation learning, and designs a Hard Sample-aware Contrastive Learning (HSCL) strategy.
A diffusion-based framework with a stage-wise multi-condition guidance mechanism that enhances both structural and textural fidelity and compares with recent image-to-image translation and diffusion-based baselines to observe competitive performance in both visual coherence and identity preservation.
A novel image inpainting framework based on a Multi-Scale Parallel Dense Connection Network (MSPDCN) with holistically nested edge detection first employed to extract structural priors and estimate edge information of missing regions, which provides guidance for subsequent reconstruction and alleviates boundary blurrin...
An efficient aligned kernel network (AKNet) is proposed, which innovatively employs super-large convolution kernels to capture global receptive fields with minimal computational overhead, effectively mimicking the long-range dependency modeling of transformers.
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