DPCA-Net establishes a superior Pareto front between restoration fidelity and efficiency by maintaining real-time inference speeds with a minimal footprint of 15.00 GFLOPs, offering a compelling practical solution for real-time all-in-one image restoration.
This paper proposes AdaptIR, an efficient all-in-one image restoration network equipped with adaptive frequency enhancement, which achieves state-of-the-art performance on all-in-one image restoration tasks involving multiple degradations, while maintaining high computational efficiency.
Yuning Cui, Wen-Qi Ren, Alois C. Knoll· IEEE Transactions on Image P...· 1 citation
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.
Wan Li, Xiao-Lin Zhang· The Visual Computer· 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 mi...
Tianyu Zhu, Ying Fu, He-Song Li et al.· IEEE Transactions on Pattern...· 0 citations
FIT employs a lightweight Degradation Encoder to predict a global degradation vector and a spatial degradation map from local degradation severity, which jointly condition the patch embedding and unembedding through adaptive deformation, and introduces a task-token dropout strategy that regularizes task conditioning du...
Zi-Hao He, Yunfeng Wu, Xinchao Wang et al.· 0 citations
Results indicate that the proposed framework provides an effective solution with substantially reduced boundary discontinuities for advanced neural image compression systems based on hybrid CNN-Transformer architectures.
S. Buthelezi, Jules R. Tapamo· IEEE Access· 0 citations
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
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