Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild
- URL: http://arxiv.org/abs/2211.14017v1
- Date: Fri, 25 Nov 2022 10:47:19 GMT
- Title: Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild
- Authors: Jucai Zhai, Pengcheng Zeng, Chihao Ma, Yong Zhao, Jie Chen
- Abstract summary: We propose a novel defocus deblurring method that uses the guidance of the defocus map to implement image deblurring.
The proposed method consists of a learnable blur kernel to estimate the defocus map, and a single-image defocus deblurring generative adversarial network (DefocusGAN) for the first time.
- Score: 9.246199263116067
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent research showed that the dual-pixel sensor has made great progress in
defocus map estimation and image defocus deblurring. However, extracting
real-time dual-pixel views is troublesome and complex in algorithm deployment.
Moreover, the deblurred image generated by the defocus deblurring network lacks
high-frequency details, which is unsatisfactory in human perception. To
overcome this issue, we propose a novel defocus deblurring method that uses the
guidance of the defocus map to implement image deblurring. The proposed method
consists of a learnable blur kernel to estimate the defocus map, which is an
unsupervised method, and a single-image defocus deblurring generative
adversarial network (DefocusGAN) for the first time. The proposed network can
learn the deblurring of different regions and recover realistic details. We
propose a defocus adversarial loss to guide this training process. Competitive
experimental results confirm that with a learnable blur kernel, the generated
defocus map can achieve results comparable to supervised methods. In the
single-image defocus deblurring task, the proposed method achieves
state-of-the-art results, especially significant improvements in perceptual
quality, where PSNR reaches 25.56 dB and LPIPS reaches 0.111.
Related papers
- Reblurring-Guided Single Image Defocus Deblurring: A Learning Framework with Misaligned Training Pairs [65.25002116216771]
We introduce a reblurring-guided learning framework for single image defocus deblurring.
Our reblurring module ensures spatial consistency between the deblurred image, the reblurred image and the input blurry image.
We have collected a new dataset specifically for single image defocus deblurring with typical misalignments.
arXiv Detail & Related papers (2024-09-26T12:37:50Z) - Exploring Deep Learning Image Super-Resolution for Iris Recognition [50.43429968821899]
We propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN)
We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms.
arXiv Detail & Related papers (2023-11-02T13:57:48Z) - A Novel Defocus-Blur Region Detection Approach Based on DCT Feature and
PCNN Structure [4.086098684345016]
This research proposes a novel and hybrid-focused detection approach based on Discrete Cosine Transform (DCT) coefficients and PC Neural Net (PCNN) structure.
The visual and quantitative evaluation illustrates that the proposed approach outperformed in terms of accuracy and efficiency to referenced algorithms.
arXiv Detail & Related papers (2023-10-12T10:58:10Z) - Single-image Defocus Deblurring by Integration of Defocus Map Prediction
Tracing the Inverse Problem Computation [25.438654895178686]
We propose a simple but effective network with spatial modulation based on the defocus map.
Experimental results show that our method can achieve better quantitative and qualitative evaluation performance than the existing state-of-the-art methods.
arXiv Detail & Related papers (2022-07-07T02:15:33Z) - Learning to Deblur using Light Field Generated and Real Defocus Images [4.926805108788465]
Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur.
We propose a novel deep defocus deblurring network that leverages the strength and overcomes the shortcoming of light fields.
arXiv Detail & Related papers (2022-04-01T11:35:51Z) - Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image [54.10957300181677]
We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image's defocus map.
Our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.
arXiv Detail & Related papers (2021-10-12T00:09:07Z) - Single image deep defocus estimation and its applications [82.93345261434943]
We train a deep neural network to classify image patches into one of the 20 levels of blurriness.
The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter.
The result is a defocus map that carries the information of the degree of blurriness for each pixel.
arXiv Detail & Related papers (2021-07-30T06:18:16Z) - Defocus Blur Detection via Salient Region Detection Prior [11.5253648614748]
Defocus blur Detection aims to separate the out-of-focus and depth-of-field areas in photos.
We propose a novel network for defocus blur detection.
arXiv Detail & Related papers (2020-11-19T05:56:11Z) - Defocus Blur Detection via Depth Distillation [64.78779830554731]
We introduce depth information into DBD for the first time.
In detail, we learn the defocus blur from ground truth and the depth distilled from a well-trained depth estimation network.
Our approach outperforms 11 other state-of-the-art methods on two popular datasets.
arXiv Detail & Related papers (2020-07-16T04:58:09Z) - Rapid Whole Slide Imaging via Learning-based Two-shot Virtual
Autofocusing [57.90239401665367]
Whole slide imaging (WSI) is an emerging technology for digital pathology.
We propose the concept of textitvirtual autofocusing, which does not rely on mechanical adjustment to conduct refocusing.
arXiv Detail & Related papers (2020-03-14T13:40:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.