Camera shake or target movement often leads to undesired blur effects in videos captured by a hand-held camera. Despite significant efforts having been devoted to video-deblur research, two major challenges remain: 1) how to model the spatio-temporal characteristics across both the spatial domain (i.e., image plane) and temporal domain (i.e., neighboring frames), and 2) how to restore sharp image details w.r.t. the conventionally adopted metric of pixel-wise errors. In this paper, to address the first challenge, we propose a DeBLuRring Network (DBLRNet) for spatial-temporal learning by applying a modified 3D convolution to both spatial and temporal domains. Our DBLRNet is able to capture jointly spatial and temporal information encoded in neighboring frames, which directly contributes to improved video deblur performance. To tackle the second challenge, we leverage the developed DBLRNet as a generator in the GAN (generative adversarial network) architecture, and employ a content loss in addition to an adversarial loss for efficient adversarial training. The developed network, which we name as DeBLuRring Generative Adversarial Network (DBLRGAN), is tested on two standard benchmarks and achieves the state-of-the-art performance.

Adversarial Spatio-Temporal Learning for Video Deblurring

An exact method of correcting the rolling shutter (RS) effect requires recovering the underlying geometry, i.e. the scene structures and the camera motions between scanlines or between views. However, the multiple-view geometry for RS cameras is much more complicated than its global shutter (GS) counterpart, with various degeneracies. In this paper, we first make a theoretical contribution by showing that RS two-view geometry is degenerate in the case of pure translational camera motion. In view of the complex RS geometry, we then propose a Convolutional Neural Network (CNN)-based method which learns the underlying geometry (camera motion and scene structure) from just a single RS image and perform RS image correction. We call our method structure-and-motion-aware RS correction because it reasons about the concealed motions between the scanlines as well as the scene structure. Our method learns from a large-scale dataset synthesized in a geometrically meaningful way where the RS effect is generated in a manner consistent with the camera motion and scene structure. In extensive experiments, our method achieves superior performance compared to other state-of-the-art methods for single image RS correction and subsequent Structure from Motion (SfM) applications.

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Learning Structure-And-Motion-Aware Rolling Shutter Correction

We present a novel network for rolling shutter effect correction. Our network takes two consecutive rolling shutter images and estimates the corresponding global shutter image of the latest frame. The dense displacement field from a rolling shutter image to its corresponding global shutter image is estimated via a motion estimation network. The learned feature representation of a rolling shutter image is then warped, via the displacement field, to its global shutter representation by a differentiable forward warping block. An image decoder recovers the global shutter image based on the warped feature representation. Our network can be trained end-to-end and only requires the global shutter image for supervision. Since there is no public dataset available, we also propose two large datasets: the Carla-RS dataset and the Fastec-RS dataset. Experimental results demonstrate that our network outperforms the state-of-the-art methods. We make both our code and datasets available at https://github.com/ethliup/DeepUnrollNet.

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Deep Shutter Unrolling Network

Joint rolling shutter correction and deblurring (RSCD) techniques are critical for the prevalent CMOS cameras. However, current approaches are still based on conventional energy optimization and are developed for static scenes. To enable learning-based approaches to address real-world RSCD problem, we contribute the first dataset, BS-RSCD, which includes both ego-motion and object-motion in dynamic scenes. Real distorted and blurry videos with corresponding ground truth are recorded simultaneously via a beam-splitter-based acquisition system.Since direct application of existing individual rolling shutter correction (RSC) or global shutter deblurring (GSD) methods on RSCD leads to undesirable results due to inherent flaws in the network architecture, we further present the first learning-based model (JCD) for RSCD. The key idea is that we adopt bi-directional warping streams for displacement compensation, while also preserving the non-warped deblurring stream for details restoration. The experimental results demonstrate that JCD achieves state-of-the-art performance on the realistic RSCD dataset (BS-RSCD) and the synthetic RSC dataset (Fastec-RS). The dataset and code are available at https://github.com/zzh-tech/RSCD.

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Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes

Most consumer cameras are equipped with electronic rolling shutter, leading to image distortions when the camera moves during image capture. We explore a surprisingly simple camera configuration that makes it possible to undo the rolling shutter distortion: two cameras mounted to have different rolling shutter directions. Such a setup is easy and cheap to build and it possesses the geometric constraints needed to correct rolling shutter distortion using only a sparse set of point correspondences between the two images. We derive equations that describe the underlying geometry for general and special motions and present an efficient method for finding their solutions. Our synthetic and real experiments demonstrate that our approach is able to remove large rolling shutter distortions of all types without relying on any specific scene structure.

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From Two Rolling Shutters to One Global Shutter

A vast majority of contemporary cameras employ rolling shutter (RS) mechanism to capture images. Due to the sequential mechanism, images acquired with a moving camera are subjected to rolling shutter effect which manifests as geometric distortions. In this work, we consider the specific scenario of a fast moving camera wherein the rolling shutter distortions not only are predominant but also become depth-dependent which in turn results in intra-frame occlusions. To this end, we develop a first-of-its-kind pipeline to recover the latent image of a 3D scene from a set of such RS distorted images. The proposed approach sequentially recovers both the camera motion and scene structure while accounting for RS and occlusion effects. Subsequently, we perform depth and occlusion-aware rectification of RS images to yield the desired latent image. Our experiments on synthetic and real image sequences reveal that the proposed approach achieves state-of-the-art results.

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Occlusion-Aware Rolling Shutter Rectification of 3D Scenes

Most present-day imaging devices are equipped with CMOS sensors. Motion blur is a common artifact in handheld cameras. Because CMOS sensors mostly employ a rolling shutter (RS), the motion deblurring problem takes on a new dimension. Although few works have recently addressed this problem, they suffer from many constraints including heavy computational cost, need for precise sensor information, and inability to deal with wide-angle systems (which most cell-phone and drone cameras are) and irregular camera trajectory. In this work, we propose a model for RS blind motion deblurring that mitigates these issues significantly. Comprehensive comparisons with state-of-the-art methods reveal that our approach not only exhibits significant computational gains and unconstrained functionality but also leads to improved deblurring performance.

Going Unconstrained with Rolling Shutter Deblurring

Dual-lens (DL) cameras capture depth information, and hence enable several important vision applications. Most present-day DL cameras employ unconstrained settings in the two views in order to support extended functionalities. But a natural hindrance to their working is the ubiquitous motion blur encountered due to camera motion, object motion, or both. However, there exists not a single work for the prospective unconstrained DL cameras that addresses this problem (so called dynamic scene deblurring). Due to the unconstrained settings, degradations in the two views need not be the same, and consequently, naive deblurring approaches produce inconsistent left-right views and disrupt scene-consistent disparities. In this paper, we address this problem using Deep Learning and make three important contributions. First, we address the root cause of view-inconsistency in standard deblurring architectures using a Coherent Fusion Module. Second, we address an inherent problem in unconstrained DL deblurring that disrupts scene-consistent disparities by introducing a memory-efficient Adaptive Scale-space Approach. This signal processing formulation allows accommodation of different image-scales in the same network without increasing the number of parameters. Finally, we propose a module to address the Space-variant and Image-dependent nature of dynamic scene blur. We experimentally show that our proposed techniques have substantial practical merit.

Deep Dynamic Scene Deblurring for Unconstrained Dual-Lens Cameras

The increasing popularity of computational light field (LF) cameras has necessitated the need for tackling motion blur which is a ubiquitous phenomenon in hand-held photography. The state-of-the-art method for blind deblurring of LFs of general 3D scenes is limited to handling only downsampled LF, both in spatial and angular resolution. This is due to the computational overhead involved in processing data-hungry full-resolution 4D LF altogether. Moreover, the method warrants high-end GPUs for optimization and is ineffective for wide-angle settings and irregular camera motion. In this paper, we introduce a new blind motion deblurring strategy for LFs which alleviates these limitations significantly. Our model achieves this by isolating 4D LF motion blur across the 2D subaperture images, thus paving the way for independent deblurring of these subaperture images. Furthermore, our model accommodates common camera motion parameterization across the subaperture images. Consequently, blind deblurring of any single subaperture image elegantly paves the way for cost-effective non-blind deblurring of the other subaperture images. Our approach is CPU-efficient computationally and can effectively deblur full-resolution LFs.

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Divide and Conquer for Full-Resolution Light Field Deblurring

We address the challenging problem of segmenting dynamic objects given a single space-variantly blurred image of a 3D scene captured using a hand-held camera. The blur induced at a particular pixel on a moving object is due to the combined effects of camera motion, the object’s own independent motion during exposure, its relative depth in the scene, and defocusing due to lens settings. We develop a deep convolutional neural network (CNN) to predict the probabilistic distribution of the composite kernel which is the convolution of motion blur and defocus kernels at each pixel. Based on the defocus component, we segment the image into different depth layers. We then judiciously exploit the motion component present in the composite kernels to automatically segment dynamic objects at each depth layer. Jointly handling defocus and motion blur enables us to resolve depth-motion ambiguity which has been a major limitation of the existing segmentation algorithms. Experimental evaluations on synthetic and real data reveal that our method significantly outperforms contemporary techniques.

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M R Mahesh Mohan

Papers

Occlusion-Aware Rolling Shutter Rectification of 3D Scenes

Going Unconstrained with Rolling Shutter Deblurring

Deep Dynamic Scene Deblurring for Unconstrained Dual-Lens Cameras

Divide and Conquer for Full-Resolution Light Field Deblurring

Deep Decoupling of Defocus and Motion Blur for Dynamic Segmentation