Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling for Motion Deblurring

• URL: http://arxiv.org/abs/2402.06117v1
• Date: Fri, 9 Feb 2024 01:00:09 GMT
• Title: Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling for Motion Deblurring
• Authors: Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan
• Abstract summary: We propose a pixel adaptive and feature attentive design for handling large blur variations across different spatial locations. We show that our approach performs favorably against the state-of-the-art deblurring algorithms.
• Score: 34.751361664891235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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