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 the temporal domain (i.e., neighboring frames) and 2) how to restore sharp image details with respect to 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 3D convolution to both the spatial and temporal domains. Our DBLRNet is able to capture jointly spatial and temporal information encoded in neighboring frames, which directly contributes to the improved video deblur performance. To tackle the second challenge, we leverage the developed DBLRNet as a generator in the generative adversarial network (GAN) architecture and employ a content loss in addition to an adversarial loss for efficient adversarial training. The developed network, which we name as deblurring GAN, is tested on two standard benchmarks and achieves the state-of-the-art performance.
Some researchers attempt to use aggregation methods to alleviate blur. @cite_39 propose a lucky image system, which constructs a final image based on the best pixels from different low quality images. @cite_35 use patch-based synthesis to restore blurry regions and ensure that the deblurred frames are spatially and temporally coherent. Motivated from the physiological fact, an efficient Fourier aggregation method is proposed in @cite_32 , which creates a consistently registered version of neighboring frames, and then fuses these frames in the Fourier domain.
@cite_35 @cite_32 @cite_39
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In this paper, we explore the structure of light field (LF) and efficiently improve the performance of the pseudo sequence based lenslet image compression, by optimized sub-view rearrangement, enhanced illumination compensation and adaptive reconstruction filtering. First, the decomposed sub-view images are rearranged into a pseudo sequence according to our optimized scan order based on sub-view correlation. Second, the generated pseudo sequence is compressed with JEM codec in which we enhance the illumination compensation by adaptively selecting reference pixels used in parameter derivation. Finally, to reduce the distortions in lenslet image decomposition and reconstruction, we propose an lenslet reconstruction method by applying adaptive filters to the reconstructed lenslet images to compensate the reconstruction errors. Each filter is derived by minimizing the distortions between the original and reconstructed pixels with same geolocation in the lenslet image. Extensive experimental results show that the proposed method achieves up to 53.7 bit rate reduction over HEVC intra coding and 34.8 over JEM intra coding in terms of BDBR. Numerous recent approaches attempt to remove image blur due to camera shake, either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem. If the photographer takes a burst of images, a modality available in virtually all modern digital cameras, we show that it is possible to combine them to get a clean sharp version. This is done without explicitly solving any blur estimation and subsequent inverse problem. The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method's rationale is that camera shake has a random nature and therefore each image in the burst is generally blurred differently. Experiments with real camera data show that the proposed Fourier Burst Accumulation algorithm achieves state-of-the-art results an order of magnitude faster, with simplicity for on-board implementation on camera phones. This paper addresses the problem of restoring images subjected to unknown and spatially varying blur caused by defocus or linear (say, horizontal) motion. The estimation of the global (non-uniform) image blur is cast as a multi-label energy minimization problem. The energy is the sum of unary terms corresponding to learned local blur estimators, and binary ones corresponding to blur smoothness. Its global minimum is found using Ishikawa's method by exploiting the natural order of discretized blur values for linear motions and defocus. Once the blur has been estimated, the image is restored using a robust (non-uniform) deblurring algorithm based on sparse regularization with global image statistics. The proposed algorithm outputs both a segmentation of the image into uniform-blur layers and an estimate of the corresponding sharp image. We present qualitative results on real images, and use synthetic data to quantitatively compare our approach to the publicly available implementation of Chakrabarti et al We present a new algorithm for removing motion blur from a single image. Our method computes a deblurred image using a unified probabilistic model of both blur kernel estimation and unblurred image restoration. We present an analysis of the causes of common artifacts found in current deblurring methods, and then introduce several novel terms within this probabilistic model that are inspired by our analysis. These terms include a model of the spatial randomness of noise in the blurred image, as well a new local smoothness prior that reduces ringing artifacts by constraining contrast in the unblurred image wherever the blurred image exhibits low contrast. Finally, we describe an effficient optimization scheme that alternates between blur kernel estimation and unblurred image restoration until convergence. As a result of these steps, we are able to produce high quality deblurred results in low computation time. We are even able to produce results of comparable quality to techniques that require additional input images beyond a single blurry photograph, and to methods that require additional hardware.