2026· IEEE Signal Processing Letters· Vol 33, pp. 3337-3341· 0 citations· 20 references
Computer Science
Abstract
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 these essential person regions. To address these challenges, the research introduces a novel framework titled Person Region Restoration Driven by Perceptual Fidelity (PRRDPF), which combines long-range dependency features with perceptual structure loss for enhanced generative restoration. Specifically, first, the research constructs a high-fidelity distorted person-region dataset via a closed-loop degradation pipeline, addressing the lack of paired datasets. Secondly, a Temporal Gated Fusion (TGF) block is designed to use gated convolutions for selectively recovering high-frequency features while capturing local and global dependencies. Finally, a Structural Similarity Index Measure (SSIM)-based dynamic weighted adversarial loss is proposed to prioritize the restoration of visual texture details. Experimental results validate that PRRDPF significantly outperforms the best models in Peak Signal-to-Noise Ratio (PSNR), SSIM, and Learned Perceptual Image Patch Similarity (LPIPS), effectively mitigating artifacts and enhancing clarity in person visuals. This framework presents a promising approach for intelligent video coding integrated with generative artificial intelligence and holds significant potential for practical applications.
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.
The confidence-guided hybrid network (CGHNet) is proposed, a parallel three-branch framework that jointly performs frequency-decoupled local restoration, global context modeling, and pixel-wise degradation prior estimation and its key component is a confidence-guided feature purification mechanism.
Xiaohui Kou, Yang Yan, Qiuyan Wang et al.· Journal of Supercomputing· 0 citations
This paper proposes BinRVR, a binarized RAW video restoration framework that reduces computation and parameters by approximately 96% while incurring only about 4% performance degradation, and develops a Distribution-Aware Binarized Convolution (DAB-Conv) that leverages the statistics of full-precision activations to mitigate quantization errors.
Tianyu Zhu, Ying Fu, Hesong Li et al.· IEEE Transactions on Pattern...· 0 citations
Joint photographic experts group (JPEG) is one of the most widely used image compression standards, but its lossy nature often introduces visible artifacts such as blocking, ringing, and blurring, particularly at lower quality factors. These degradations significantly reduce perceptual quality and affect downstream computer vision tasks. To address these limitations, in this study work a CNN-based edge-aware artifact reduction framework (CNN-AR) is proposed that integrates an enhanced deep super-resolution (EDSR) backbone with a holistically nested edge detection (HED) guided loss. This design enforces both pixel fidelity and edge consistency, enabling superior artifact suppression while preserving fine structural details. Extensive experiments conducted on benchmark datasets (LIVE1, Kodak, Set14, Classic5, and CLIC) across quality factors 10–40 demonstrate the effectiveness of the proposed approach. Compared to state-ofthe-art models including ARCNN, DnCNN, and DPW-SDNet, the proposed method consistently achieves higher perceptual scores. On average, CNN-AR improves PSNR by +0.38 dB, Structural Similarity Index (SSIM) by +0.012, MS-SSIM by +0.009, and PSNR-B by +0.41 dB across datasets, shows its ability to deliver both numerically superior and visually sharper reconstructions.
Nupur, Nishant Kumar, Sajal Suhane et al.· International Journal of Onl...· 0 citations
PixRestore is presented, a VAE-free pixel-space Diffusion Transformer (DiT) for UIR, where the diffusion backbone is trained entirely from scratch, without relying on T2I pretraining.
Lingchen Sun, Rongyuan Wu, Xiangtao Kong et al.· 0 citations
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