SVBR-NET: A Non-Blind Spatially Varying Defocus Blur Removal Network

• URL: http://arxiv.org/abs/2206.12930v1
• Date: Sun, 26 Jun 2022 17:21:12 GMT
• Title: SVBR-NET: A Non-Blind Spatially Varying Defocus Blur Removal Network
• Authors: Ali Karaali and Claudio Rosito Jung
• Abstract summary: We propose a non-blind approach for image deblurring that can deal with spatially-varying kernels. We introduce two encoder-decoder sub-networks that are fed with the blurry image and the estimated blur map. The network is trained with synthetically blur kernels that are augmented to emulate blur maps produced by existing blur estimation methods.
• Score: 2.4975981795360847
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Defocus blur is a physical consequence of the optical sensors used in most cameras. Although it can be used as a photographic style, it is commonly viewed as an image degradation modeled as the convolution of a sharp image with a spatially-varying blur kernel. Motivated by the advance of blur estimation methods in the past years, we propose a non-blind approach for image deblurring that can deal with spatially-varying kernels. We introduce two encoder-decoder sub-networks that are fed with the blurry image and the estimated blur map, respectively, and produce as output the deblurred (deconvolved) image. Each sub-network presents several skip connections that allow data propagation from layers spread apart, and also inter-subnetwork skip connections that ease the communication between the modules. The network is trained with synthetically blur kernels that are augmented to emulate blur maps produced by existing blur estimation methods, and our experimental results show that our method works well when combined with a variety of blur estimation methods.

Related papers

• Self-Supervised Multi-Scale Network for Blind Image Deblurring via Alternating Optimization [12.082424048578753]
  We present a self-supervised multi-scale blind image deblurring method to jointly estimate the latent image and the blur kernel. Thanks to the collaborative estimation across multiple scales, our method avoids the computationally intensive coarse-to-fine propagation and additional image deblurring processes.
  arXiv  Detail & Related papers  (2024-09-02T07:08:17Z)
• Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks [0.0]
  We propose a learning-based motion deblurring method based on dense non-uniform motion blur estimation. We train the networks on sharp/blurry pairs synthesized according to a convolution-based, non-uniform motion blur degradation model.
  arXiv  Detail & Related papers  (2023-08-05T20:23:13Z)
• Collaborative Blind Image Deblurring [15.555393702795076]
  We show that when extracting patches of similar underlying blur is possible, jointly processing the stack of patches yields superior accuracy than handling them separately. We present three practical patch extraction strategies for image sharpening, camera shake removal and optical aberration correction, and validate the proposed approach on both synthetic and real-world benchmarks.
  arXiv  Detail & Related papers  (2023-05-25T13:14:29Z)
• Learning Single Image Defocus Deblurring with Misaligned Training Pairs [80.13320797431487]
  We propose a joint deblurring and reblurring learning framework for single image defocus deblurring. Our framework can be applied to boost defocus deblurring networks in terms of both quantitative metrics and visual quality.
  arXiv  Detail & Related papers  (2022-11-26T07:36:33Z)
• MC-Blur: A Comprehensive Benchmark for Image Deblurring [127.6301230023318]
  In most real-world images, blur is caused by different factors, e.g., motion and defocus. We construct a new large-scale multi-cause image deblurring dataset (called MC-Blur) Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios.
  arXiv  Detail & Related papers  (2021-12-01T02:10:42Z)
• Explore Image Deblurring via Blur Kernel Space [17.67255729783263]
  We propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen blur operator by a kernel in the encoded space and searches for the corresponding sharp image. Our method can be used for blur synthesis by transferring existing blur operators from a given dataset into a new domain.
  arXiv  Detail & Related papers  (2021-04-01T07:52:53Z)
• DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring [66.91879314310842]
  We propose an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale cascaded feature refinement module then predicts the deblurred image from the deconvolved deep features. We show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts and quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin.
  arXiv  Detail & Related papers  (2021-03-18T00:38:11Z)
• Blur-Attention: A boosting mechanism for non-uniform blurred image restoration [27.075713246257596]
  We propose a blur-attention module to dynamically capture the spatially varying features of non-uniform blurred images. By introducing the blur-attention network into a conditional generation adversarial framework, we propose an end-to-end blind motion deblurring method. Experimental results show that the deblurring capability of our method achieved outstanding objective performance in terms of PSNR, SSIM, and subjective visual quality.
  arXiv  Detail & Related papers  (2020-08-19T16:07:06Z)
• Learning to See Through Obstructions with Layered Decomposition [117.77024641706451]
  We present a learning-based approach for removing unwanted obstructions from moving images. Our method leverages motion differences between the background and obstructing elements to recover both layers. We show that the proposed approach learned from synthetically generated data performs well to real images.
  arXiv  Detail & Related papers  (2020-08-11T17:59:31Z)
• Deep Blind Video Super-resolution [85.79696784460887]
  We propose a deep convolutional neural network (CNN) model to solve video SR by a blur kernel modeling approach. The proposed CNN model consists of motion blur estimation, motion estimation, and latent image restoration modules. We show that the proposed algorithm is able to generate clearer images with finer structural details.
  arXiv  Detail & Related papers  (2020-03-10T13:43:24Z)
• Self-Supervised Linear Motion Deblurring [112.75317069916579]
  Deep convolutional neural networks are state-of-the-art for image deblurring. We present a differentiable reblur model for self-supervised motion deblurring. Our experiments demonstrate that self-supervised single image deblurring is really feasible.
  arXiv  Detail & Related papers  (2020-02-10T20:15:21Z)

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.