BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring

• URL: http://arxiv.org/abs/2105.14766v1
• Date: Mon, 31 May 2021 07:55:30 GMT
• Title: BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
• Authors: Pengwei Liang, Junjun Jiang, Xianming Liu, and Jiayi Ma
• Abstract summary: convolutional neural networks (CNNs) have been introduced to the defocus deblurring problem and achieved significant progress. This study designs a novel blur-aware multi-branch network (BaMBNet) in which different regions (with different blur amounts) should be treated differentially. Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art methods.
• Score: 74.34263243089688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The defocus deblurring raised from the finite aperture size and exposure time is an essential problem in the computational photography. It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods. Due to its great breakthrough in low-level tasks, convolutional neural networks (CNNs) have been introduced to the defocus deblurring problem and achieved significant progress. However, they apply the same kernel for different regions of the defocus blurred images, thus it is difficult to handle these nonuniform blurred images. To this end, this study designs a novel blur-aware multi-branch network (BaMBNet), in which different regions (with different blur amounts) should be treated differentially. In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the DP data, which measures the defocus disparity between the left and right views. Based on the assumption that different image regions with different blur amounts have different deblurring difficulties, we leverage different networks with different capacities (\emph{i.e.} parameters) to process different image regions. Moreover, we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch. In this way, we can expect to well maintain the information of the clear regions while recovering the missing details of the blurred regions. Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art methods. Source code will be available at https://github.com/junjun-jiang/BaMBNet.

Related papers

• Multi-spectral Class Center Network for Face Manipulation Detection and Localization [52.569170436393165]
  We propose a novel Multi-Spectral Class Center Network (MSCCNet) for face manipulation detection and localization. Based on the features of different frequency bands, the MSCC module collects multi-spectral class centers and computes pixel-to-class relations. Applying multi-spectral class-level representations suppresses the semantic information of the visual concepts which is insensitive to manipulated regions of forgery images.
  arXiv  Detail & Related papers  (2023-05-18T08:09:20Z)
• Redesigning Multi-Scale Neural Network for Crowd Counting [68.674652984003]
  We introduce a hierarchical mixture of density experts, which hierarchically merges multi-scale density maps for crowd counting. Within the hierarchical structure, an expert competition and collaboration scheme is presented to encourage contributions from all scales. Experiments show that our method achieves the state-of-the-art performance on five public datasets.
  arXiv  Detail & Related papers  (2022-08-04T21:49:29Z)
• Learning Depth from Focus in the Wild [16.27391171541217]
  We present a convolutional neural network-based depth estimation from single focal stacks. Our method allows depth maps to be inferred in an end-to-end manner even with image alignment. For the generalization of the proposed network, we develop a simulator to realistically reproduce the features of commercial cameras.
  arXiv  Detail & Related papers  (2022-07-20T05:23:29Z)
• 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)
• An Empirical Method to Quantify the Peripheral Performance Degradation in Deep Networks [18.808132632482103]
  convolutional neural network (CNN) kernels compound with each convolutional layer. Deeper and deeper networks combined with stride-based down-sampling means that the propagation of this region can end up covering a non-negligable portion of the image. Our dataset is constructed by inserting objects into high resolution backgrounds, thereby allowing us to crop sub-images which place target objects at specific locations relative to the image border. By probing the behaviour of Mask R-CNN across a selection of target locations, we see clear patterns of performance degredation near the image boundary, and in particular in the image corners.
  arXiv  Detail & Related papers  (2020-12-04T18:00:47Z)
• 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)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

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.