Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.
|ジャーナル||IS and T International Symposium on Electronic Imaging Science and Technology|
|出版ステータス||Published - 2020 1 26|
|イベント||18th Image Processing: Algorithms and Systems Conference, IPAS 2020 - Burlingame, United States|
継続期間: 2020 1 26 → 2020 1 30
ASJC Scopus subject areas
- コンピュータ グラフィックスおよびコンピュータ支援設計
- コンピュータ サイエンスの応用