Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling for Motion Deblurring
<|reference_start|>Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling for Motion Deblurring: This paper tackles the problem of motion deblurring of dynamic scenes. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in non-uniform motion deblurring, their performance-complexity trade-off is still sub-optimal. Most existing approaches achieve a large receptive field by increasing the number of generic convolution layers and kernel size. In this work, we propose a pixel adaptive and feature attentive design for handling large blur variations across different spatial locations and process each test image adaptively. We design a content-aware global-local filtering module that significantly improves performance by considering not only global dependencies but also by dynamically exploiting neighboring pixel information. We further introduce a pixel-adaptive non-uniform sampling strategy that implicitly discovers the difficult-to-restore regions present in the image and, in turn, performs fine-grained refinement in a progressive manner. Extensive qualitative and quantitative comparisons with prior art on deblurring benchmarks demonstrate that our approach performs favorably against the state-of-the-art deblurring algorithms.<|reference_end|>
@article{suin2024spatially-attentive,
    title={Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling
  for Motion Deblurring},
    author={Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan},
    journal={arXiv preprint arXiv:2402.06117},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06117},
    primaryClass={cs.CV}
}