Adaptive Single Image Deblurring (original) (raw)
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Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
This paper tackles the problem of motion deblurring of dynamic scenes. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in nonuniform motion deblurring, their performance-complexity trade-off is still sub-optimal. Existing approaches achieve a large receptive field by increasing the number of generic convolution layers and kernel-size, but this comes at the expense of of the increase in model size and inference speed. In this work, we propose an efficient pixel adaptive and feature attentive design for handling large blur variations across different spatial locations and process each test image adaptively. We also propose an effective content-aware global-local filtering module that significantly improves performance by considering not only global dependencies but also by dynamically exploiting neighboring pixel information. We use a patch-hierarchical attentive architecture composed of the above module that implicitly discovers the spatial variations in the blur present in the input image and in turn, performs local and global modulation of intermediate features. Extensive qualitative and quantitative comparisons with prior art on deblurring benchmarks demonstrate that our design offers significant improvements over the state-of-the-art in accuracy as well as speed.
Spatially-Adaptive Residual Networks for Efficient Image and Video Deblurring
2019
In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur. Restoration of images affected by severe blur necessitates a network design with a large receptive field, which existing networks attempt to achieve through a simple increment in the number of generic convolution layers, kernel-size, or the scales at which the image is processed. However, increasing the network capacity in this manner comes at the expense of an increase in model size and inference time, and ignoring the non-uniform nature of blur. We present a new architecture composed of spatially adaptive residual learning modules that implicitly discover the spatially varying shifts responsible for non-uniform blur in the input image and learn to modulate the filters. This capability is complemented by a self-attentive module which captures non-local relationships among the intermediate features and enhances the receptive field. We then incorporate a spatiotemporal recurrent module i...
Motion Deblurring with an Adaptive Network
2019
In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur. Restoration of images affected by severe blur necessitates a network design with a large receptive field, which existing networks attempt to achieve through simple increment in the number of generic convolution layers, kernel-size, or the scales at which the image is processed. However, increasing the network capacity in this manner comes at the expense of increase in model size and inference speed, and ignoring the non-uniform nature of blur. We present a new architecture composed of spatially adaptive residual learning modules that implicitly discover the spatially varying shifts responsible for non-uniform blur in the input image and learn to modulate the filters. This capability is complemented by a self-attentive module which captures non-local relationships among the intermediate features and enhances the receptive field. We then incorporate a spatiotemporal recurrent module in th...
MC-Blur: A Comprehensive Benchmark for Image Deblurring
arXiv (Cornell University), 2021
Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios. However, in most real-world images, blur is caused by different factors, e.g., motion, and defocus. In this paper, we address how other deblurring methods perform in the case of multiple types of blur. For in-depth performance evaluation, we construct a new large-scale multicause image deblurring dataset (MC-Blur), including real-world and synthesized blurry images with different blur factors. The images in the proposed MC-Blur dataset are collected using other techniques: averaging sharp images captured by a 1000-fps highspeed camera, convolving Ultra-High-Definition (UHD) sharp images with large-size kernels, adding defocus to images, and real-world blurry images captured by various camera models. Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios, analyze their efficiency, and investigate the buildataset's capacity. These benchmarking results provide a comprehensive overview of the advantages and limitations of current deblurring methods, revealing our dataset's advances.
BaMBNet: A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring
IEEE/CAA Journal of Automatica Sinica, 2022
Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in 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, previous methods apply the same learned kernel for different regions of the defocus blurred images, thus it is difficult to handle nonuniform blurred images. To this end, this study designs a novel blur-aware multi-branch network (Ba-MBNet), in which different regions are treated differentially. In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel (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 to treat 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 maintain the information of the clear regions well while recovering the missing details of the blurred regions. Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art (SOTA) methods. For the dual-pixel defocus deblurring (DPD)-blur dataset, the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio (PSNR) and reduces learnable parameters by 85%. The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.
Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes
2021
For the success of video deblurring, it is essential to utilize information from neighboring frames. Most state-of-the-art video deblurring methods adopt motion compensation between video frames to aggregate information from multiple frames that can help deblur a target frame. However, the motion compensation methods adopted by previous deblurring methods are not blur-invariant, and consequently, their accuracy is limited for blurry frames with different blur amounts. To alleviate this problem, we propose two novel approaches to deblur videos by effectively aggregating information from multiple video frames. First, we present blur-invariant motion estimation learning to improve motion estimation accuracy between blurry frames. Second, for motion compensation, instead of aligning frames by warping with estimated motions, we use a pixel volume that contains candidate sharp pixels to resolve motion estimation errors. We combine these two processes to propose an effective recurrent vide...
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019
This paper proposes a human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG). The proposed model is based on a triple-branch encoder-decoder architecture. The first two branches are learned for sharpening FG humans and BG details, respectively; while the third one produces global, harmonious results by comprehensively fusing multi-scale deblurring information from the two domains. The proposed model is further endowed with a supervised, human-aware attention mechanism in an end-toend fashion. It learns a soft mask that encodes FG human information and explicitly drives the FG/BG decoderbranches to focus on their specific domains. To further benefit the research towards Human-aware Image Deblurring, we introduce a large-scale dataset, named HIDE, which consists of 8,422 blurry and sharp image pairs with 65,784 densely annotated FG human bounding boxes. HIDE is specifically built to span a broad range of scenes, human object sizes, motion patterns, and background complexities. Extensive experiments on public benchmarks and our dataset demonstrate that our model performs favorably against the state-of-the-art motion deblurring methods, especially in capturing semantic details.
Concurrent Video Denoising and Deblurring for Dynamic Scenes
IEEE Access
Dynamic scene video deblurring is a challenging task due to the spatially variant blur inflicted by independently moving objects and camera shakes. Recent deep learning works bypass the ill-posedness of explicitly deriving the blur kernel by learning pixel-to-pixel mappings, which is commonly enhanced by larger region awareness. This is a difficult yet simplified scenario because noise is neglected when it is omnipresent in a wide spectrum of video processing applications. Despite its relevance, the problem of concurrent noise and dynamic blur has not yet been addressed in the deep learning literature. To this end, we analyze existing state-of-the-art deblurring methods and encounter their limitations in handling nonuniform blur under strong noise conditions. Thereafter, we propose a first-to-date work that addresses blurand noise-free frame recovery by casting the restoration problem into a multi-task learning framework. Our contribution is threefold: a) We propose R2-D4, a multi-scale encoder architecture attached to two cascaded decoders performing the restoration task in two steps. b) We design multi-scale residual dense modules, bolstered by our modulated efficient channel attention, to enhance the encoder representations via augmenting deformable convolutions to capture longer-range and object-specific context that assists blur kernel estimation under strong noise. c) We perform extensive experiments and evaluate state-of-theart approaches on a publicly available dataset under different noise levels. The proposed method performs favorably under all noise levels while retaining a reasonably low computational and memory footprint. INDEX TERMS deblurring, denoising, multi-task learning, video enhancement VOLUME 4, 2016 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3129602, IEEE Access Efklidis Katsaros et al.: Concurrent Video Denoising and Deblurring for Dynamic Scenes Skip Connection Summation Channel Partition FIGURE 1. The proposed R2-D4 architecture restores the reference frame (R2) via cascaded denoising and deblurring (D2) after aligning its features with the neighboring ones via the dense deformable (D2) alignment module.
Deblurring by Realistic Blurring
arXiv (Cornell University), 2020
Existing deep learning methods for image deblurring typically train models using pairs of sharp images and their blurred counterparts. However, synthetically blurring images do not necessarily model the genuine blurring process in real-world scenarios with sufficient accuracy. To address this problem, we propose a new method which combines two GAN models, i.e., a learning-to-Blur GAN (BGAN) and learning-to-DeBlur GAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images. The first model, BGAN, learns how to blur sharp images with unpaired sharp and blurry image sets, and then guides the second model, DBGAN, to learn how to correctly deblur such images. In order to reduce the discrepancy between real blur and synthesized blur, a relativistic blur loss is leveraged. As an additional contribution, this paper also introduces a Real-World Blurred Image (RWBI) dataset including diverse blurry images. Our experiments show that the proposed method achieves consistently superior quantitative performance as well as higher perceptual quality on both the newly proposed dataset and the public GOPRO dataset.
Dynamic Scene Deblurring by Depth Guided Model
IEEE Transactions on Image Processing, 2020
Dynamic scene blur is usually caused by object motion, depth variation as well as camera shake. Most existing methods usually solve this problem using image segmentation or fully end-to-end trainable deep convolutional neural networks by considering different object motions or camera shakes. However, these algorithms are less effective when there exist depth variations. In this work, we propose a deep neural convolutional network that exploits the depth map for dynamic scene deblurring. Given a blurred image, we first extract the depth map and adopt a depth refinement network to restore the edges and structure in the depth map. To effectively exploit the depth map, we adopt the spatial feature transform layer to extract depth features and fuse with the image features through scaling and shifting. Our image deblurring network thus learns to restore a clear image under the guidance of the depth map. With substantial experiments and analysis, we show that the depth information is crucial to the performance of the proposed model. Finally, extensive quantitative and qualitative evaluations demonstrate that the proposed model performs favorably against the state-of-the-art dynamic scene deblurring approaches as well as conventional depth-based deblurring algorithms.