Defocus blur arises in images that are captured with a shallow depth of field due to the use of a wide aperture. Correcting defocus blur is challenging because the blur is spatially varying and difficult to estimate. We propose an effective defocus deblurring method that exploits data available on dual-pixel (DP) sensors found on most modern cameras. DP sensors are used to assist a camera's auto-focus by capturing two sub-aperture views of the scene in a single image shot. The two sub-aperture images are used to calculate the appropriate lens position to focus on a particular scene region and are discarded afterwards. We introduce a deep neural network (DNN) architecture that uses these discarded sub-aperture images to reduce defocus blur. A key contribution of our effort is a carefully captured dataset of 500 scenes (2000 images) where each scene has: (i) an image with defocus blur captured at a large aperture; (ii) the two associated DP sub-aperture views; and (iii) the corresponding all-in-focus image captured with a small aperture. Our proposed DNN produces results that are significantly better than conventional single image methods in terms of both quantitative and perceptual metrics -- all from data that is already available on the camera but ignored. The dataset, code, and trained models are available at https://github.com/Abdullah-Abuolaim/defocus-deblurring-dual-pixel.
We propose a novel end-to-end learning-based approach for single image defocus deblurring. The proposed approach is equipped with a novel Iterative Filter Adaptive Network (IFAN) that is specifically designed to handle spatially-varying and large defocus blur. For adaptively handling spatially-varying blur, IFAN predicts pixel-wise deblurring filters, which are applied to defocused features of an input image to generate deblurred features. For effectively managing large blur, IFAN models deblurring filters as stacks of small-sized separable filters. Predicted separable deblurring filters are a…
Tohoku University（东北大学）；RIKEN Center for AIP（理化学研究所人工智能中心）
In this paper, we consider the problem in defocus image deblurring. Previous classical methods follow two-steps approaches, i.e., first defocus map estimation and then the non-blind deblurring. In the era of deep learning, some researchers have tried to address these two problems by CNN. However, the simple concatenation of defocus map, which represents the blur level, leads to suboptimal performance. Considering the spatial variant property of the defocus blur and the blur level indicated in the defocus map, we employ the defocus map as conditional guidance to adjust the features from the inp…
It is a challenging task to recover sharp image from a single defocus blurry image in real-world applications. On many modern cameras, dual-pixel (DP) sensors create two-image views, based on which stereo information can be exploited to benefit defocus deblurring. Despite the impressive results achieved by existing DP defocus deblurring methods, the misalignment between DP image views is still not studied, leaving room for improving DP defocus deblurring. In this work, we propose a Dual-Pixel Alignment Network (DPANet) for defocus deblurring. Generally, DPANet is an encoder-decoder with skip-c…
Harbin Institute of Technology（哈尔滨工业大学）；Wuhan University（武汉大学）
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, thi…
Depth Estimation and Image Restoration by Deep Learning from Defocused Images
CEOSpaceTech；University POLITEHNICA of Bucharest（布加勒斯特理工大学）；Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS)（智能技术卓越研究中心（CiTIUS））；University of Santiago de Compostela（圣地亚哥德孔波斯特拉大学）
Recent work has shown impressive results on data-driven defocus deblurring using the two-image views available on modern dual-pixel (DP) sensors. One significant challenge in this line of research is access to DP data. Despite many cameras having DP sensors, only a limited number provide access to the low-level DP sensor images. In addition, capturing training data for defocus deblurring involves a time-consuming and tedious setup requiring the camera's aperture to be adjusted. Some cameras with DP sensors (e.g., smartphones) do not have adjustable apertures, further limiting the ability to pr…
City University of Hong Kong（香港城市大学）；Max-Planck-Institut für Informatik（马克斯·普朗克信息学研究所）；Stanford University（斯坦福大学）
Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur. While deep learning approach shows great promise in solving image restoration problems, defocus deblurring demands accurate training data that consists of all-in-focus and defocus image pairs, which is difficult to collect. Naive two-shot capturing cannot achieve pixel-wise correspondence between the defocused and all-in-focus image pairs. Synthetic aperture of light fields is suggested to be a more reliable way to generate accurate image pairs. However, the defocus blur generated from light field dat…
Computationally removing the motion blur introduced by camera shake or object motion in a captured image remains a challenging task in computational photography. Deblurring methods are often limited by the fixed global exposure time of the image capture process. The post-processing algorithm either must deblur a longer exposure that contains relatively little noise or denoise a short exposure that intentionally removes the opportunity for blur at the cost of increased noise. We present a novel approach of leveraging spatially varying pixel exposures for motion deblurring using next-generation …
We develop a deep convolutional neural networks(CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists of attentional encoders, triple locals and global local modules to effectively extract useful information from each image in the dual-pixels and select the useful information from each image and synthesize the final output image. We demonstrate the effectiveness of the proposed deblurring algorithm in terms of both qualitative and quantitative aspects by evaluating on the test set …
Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Renmin University of China（中国人民大学）；the Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；the Hong Kong University of Science and Technology（香港科技大学）
This paper tackles the problem of dynamic scene deblurring. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in non-uniform motion deblurring, their performance-complexity trade-off is still sub-optimal. Existing approaches achieve a large receptive field by a simple increment in the number of generic convolution layers, kernel-size, which comes with the burden 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 within and across differe…
Tencent AI Lab（腾讯AI实验室）；University of Connecticut（康涅狄格大学）；The Fourth Hospital of Hebei Medical University（河北医科大学第四医院）；West China Hospital, Sichuan University（四川大学华西医院）
Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency. However, defocus and motion blur can obscure tissue or cell characteristics hence compromising AI algorithms'accuracy and robustness in analyzing the images. In this paper, we demonstrate a deep-learning-based approach that can alleviate the defocus and motion blur of a microscopic image and output a sharper and cleaner image with retrieved fine details without prior knowledge of the blur type, blur extent and patho…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
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 non-uniform 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 …
Trinity College Dublin（都柏林圣三一学院）；ADAPT: SFI Centre for Digital Media Technology（ADAPT：SFI数字媒体技术中心）；Federal University of Rio Grande Do Sul（南里奥格兰德联邦大学）
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 (deconvolv…
Camera motion deblurring is an important low-level vision task for achieving better imaging quality. When a scene has outliers such as saturated pixels, the captured blurred image becomes more difficult to restore. In this paper, we propose a novel method to handle camera motion blur with outliers. We first propose an edge-aware scale-recurrent network (EASRN) to conduct deblurring. EASRN has a separate deblurring module that removes blur at multiple scales and an upsampling module that fuses different input scales. Then a salient edge detection network is proposed to supervise the training pr…
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Google Research（谷歌研究院）
Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead to unrealistic reconstructions. We present an alternative framework for blind deblurring based on conditional diffusion models. Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse se…
Global operations, such as global average pooling, are widely used in top-performance image restorers. They aggregate global information from input features along entire spatial dimensions but behave differently during training and inference in image restoration tasks: they are based on different regions, namely the cropped patches (from images) and the full-resolution images. This paper revisits global information aggregation and finds that the image-based features during inference have a different distribution than the patch-based features during training. This train-test inconsistency negat…
Depth information is useful in many image processing applications. However, since taking a picture is a process of projection of a 3D scene onto a 2D imaging sensor, the depth information is embedded in the image. Extracting the depth information from the image is a challenging task. A guiding principle is that the level of blurriness due to defocus is related to the distance between the object and the focal plane. Based on this principle and the widely used assumption that Gaussian blur is a good model for defocus blur, we formulate the problem of estimating the spatially varying defocus blur…
Joint Transmission and Deblurring: A Semantic Communication Approach Using Events
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；School of Electronic Information, Wuhan University（武汉大学电子信息学院）
Inner Mongolia University（内蒙古大学）；The Chinese University of Hong Kong（香港中文大学）
The parameter selection is crucial to regularization based image restoration methods. Generally speaking, a spatially fixed parameter for regularization item in the whole image does not perform well for both edge and smooth areas. A larger parameter of regularization item reduces noise better in smooth areas but blurs edge regions, while a small parameter sharpens edge but causes residual noise. In this paper, an automated spatially adaptive regularization model, which combines the harmonic and TV models, is proposed for reconstruction of noisy and blurred images. In the proposed model, it det…
Blind image deblurring is a long standing challenging problem in image processing and low-level vision. Recently, sophisticated priors such as dark channel prior, extreme channel prior, and local maximum gradient prior, have shown promising effectiveness. However, these methods are computationally expensive. Meanwhile, since these priors involved subproblems cannot be solved explicitly, approximate solution is commonly used, which limits the best exploitation of their capability. To address these problems, this work firstly proposes a simplified sparsity prior of local minimal pixels, namely p…
Synthetic aperture sonar (SAS) requires precise positional and environmental information to produce well-focused output during the image reconstruction step. However, errors in these measurements are commonly present resulting in defocused imagery. To overcome these issues, an \emph{autofocus} algorithm is employed as a post-processing step after image reconstruction for the purpose of improving image quality using the image content itself. These algorithms are usually iterative and metric-based in that they seek to optimize an image sharpness metric. In this letter, we demonstrate the potenti…
Single-photon avalanche diodes (SPADs) are a rapidly developing image sensing technology with extreme low-light sensitivity and picosecond timing resolution. These unique capabilities have enabled SPADs to be used in applications like LiDAR, non-line-of-sight imaging and fluorescence microscopy that require imaging in photon-starved scenarios. In this work we harness these capabilities for dealing with motion blur in a passive imaging setting in low illumination conditions. Our key insight is that the data captured by a SPAD array camera can be represented as a 3D spatio-temporal tensor of pho…
We propose fixed-function neural network hardware that is designed to perform pixel-to-pixel image transformations in a highly efficient way. We use a fully trainable, fixed-topology neural network to build a model that can perform a wide variety of image processing tasks. Our model uses compressed skip lines and hybrid FIR-IIR blocks to reduce the latency and hardware footprint. Our proposed Machine Learning Imaging Core, dubbed MagIC, uses a silicon area of ~3mm^2 (in TSMC 16nm), which is orders of magnitude smaller than a comparable pixel-wise dense prediction model. MagIC requires no DDR b…
Elmore Family School of Electrical and Computer Engineering, Purdue University（普渡大学埃尔莫尔家族电气与计算机工程学院）；Department of Computer Science, McCormick School of Engineering and Applied Science, Northwestern University（西北大学麦考密克工程与应用科学学院计算机科学系）
University of Electronic Science and Technology of China（电子科技大学）
Outdoor vision robotic systems and autonomous cars suffer from many image-quality issues, particularly haze, defocus blur, and motion blur, which we will define generically as "blindness issues". These blindness issues may seriously affect the performance of robotic systems and could lead to unsafe decisions being made. However, existing solutions either focus on one type of blindness only or lack the ability to estimate the degree of blindness accurately. Besides, heavy computation is needed so that these solutions cannot run in real-time on practical systems. In this paper, we provide a meth…
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）；Toronto Metropolitan University（多伦多都会大学）
BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior
Technical University of Munich（慕尼黑工业大学）；Georg-August-University Göttingen（哥廷根大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health（亥姆霍兹慕尼黑研究中心人工智能健康研究所）；Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health（亥姆霍兹慕尼黑研究中心亥姆霍兹人工智能）
针对双视角 LSFM 局部融合导致空间不一致的问题，提出 BigFUSE，利用图像形成先验和贝叶斯 EM 估计全局合焦—离焦边界，实现无结构化伪影的自动融合。
