Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Realistic Blur Synthesis for Learning Image Deblurring   Training learning-based deblurring methods demands a tremendous amount of
blurred and sharp image pairs. Unfortunately, existing synthetic datasets are
not realistic enough, and deblurring models trained on them cannot handle real
blurred images effectively. While real datasets have recently been proposed,
they provide limited diversity of scenes and camera settings, and capturing
real datasets for diverse settings is still challenging. This paper analyzes
various factors that introduce differences between real and synthetic blurred
images, and presents a novel blur synthesis pipeline to synthesize more
realistic blur. We also present RSBlur, a novel dataset with real blurred
images and the corresponding sharp image sequences to enable detailed analysis
on the differences between real and synthetic blur. With our blur synthesis
pipeline and RSBlur dataset, we reveal the effects of different factors in blur
synthesis. We also show that our synthesis method can improve the deblurring
performance on real blurred images.

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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys