Abstract-To correct geometric distortion and reduce space and time-varying blur, a new approach is proposed in this paper capable of restoring a single high-quality image from a given image sequence distorted by atmospheric turbulence. This approach reduces the space and time-varying deblurring problem to a shift invariant one. It first registers each frame to suppress geometric deformation through B-spline-based nonrigid registration. Next, a temporal regression process is carried out to produce an image from the registered frames, which can be viewed as being convolved with a space invariant near-diffraction-limited blur. Finally, a blind deconvolution algorithm is implemented to deblur the fused image, generating a final output. Experiments using real data illustrate that this approach can effectively alleviate blur and distortions, recover details of the scene, and significantly improve visual quality.

Abstract-Estimating the amount of blur in a given image is important for computer vision applications. More specifically, the spatially varying defocus point-spread-functions (PSFs) over an image reveal geometric information of the scene, and their estimate can also be used to recover an all-in-focus image. A PSF for a defocus blur can be specified by a single parameter indicating its scale. Most existing algorithms can only select an optimal blur from a finite set of candidate PSFs for each pixel. Some of those methods require a coded aperture filter inserted in the camera. In this paper we present an algorithm estimating a defocus scale map from a single image, which is applicable to conventional cameras. This method is capable of measuring the probability of local defocus scale in the continuous domain. It also takes smoothness and color edge information into consideration to generate a coherent blur map indicating the amount of blur at each pixel. Simulated and real data experiments illustrate excellent performance, and its successful applications in foreground/background segmentation.Index Terms-spatially varying blur estimation, defocus blur.

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A no-reference objective sharpness metric detecting both blur and noise is proposed in this paper. This metric is based on the local gradients of the image and does not require any edge detection. Its value drops either when the test image becomes blurred or corrupted by random noise. It can be thought of as an indicator of the signal to noise ratio of the image. Experiments using synthetic, natural, and compressed images are presented to demonstrate the effectiveness and robustness of this metric. Its statistical properties are also provided.

To correct geometric distortion and reduce blur in videos that suffer from atmospheric turbulence, a multi-frame image reconstruction approach is proposed in this paper. This approach contains two major steps. In the first step, a B-spline based non-rigid image registration algorithm is employed to register each observed frame with respect to a reference image. To improve the registration accuracy, a symmetry constraint is introduced, which penalizes inconsistency between the forward and backward deformation parameters during the estimation process. A fast Gauss-Newton implementation method is also developed to reduce the computational cost of the registration algorithm. In the second step, a high quality image is restored from the registered observed frames under a Bayesian reconstruction framework, where we use L 1 norm minimization and a bilateral total variation (BTV) regularization prior, to make the algorithm more robust to noise and estimation error. Experiments show that the proposed approach can effectively reduce the influence of atmospheric turbulence even for noisy videos with relatively long exposure time.

Abstract-Spatial domain image filters (e.g., bilateral filter, non-local means, locally adaptive regression kernel) have achieved great success in denoising. Their overall performance, however, has not generally surpassed the leading transform domainbased filters (such as BM3-D). One important reason is that spatial domain filters lack efficiency to adaptively fine tune their denoising strength; something that is relatively easy to do in transform domain method with shrinkage operators. In the pixel domain, the smoothing strength is usually controlled globally by, for example, tuning a regularization parameter. In this paper, we propose spatially adaptive iterative filtering (SAIF) 1 a new strategy to control the denoising strength locally for any spatial domain method. This approach is capable of filtering local image content iteratively using the given base filter, and the type of iteration and the iteration number are automatically optimized with respect to estimated risk (i.e., mean-squared error). In exploiting the estimated local signal-to-noise-ratio, we also present a new risk estimator that is different from the oftenemployed SURE method, and exceeds its performance in many cases. Experiments illustrate that our strategy can significantly relax the base algorithm's sensitivity to its tuning (smoothing) parameters, and effectively boost the performance of several existing denoising filters to generate state-of-the-art results under both simulated and practical conditions.

The ZiYuan‐3 surveying satellite (ZY‐3), launched on 9th January 2012, is China's first civilian high‐resolution stereo mapping satellite. To ensure the mapping accuracy of ZY‐3, considerable research has been conducted since its launch on the calibration and validation of its three‐line array charge‐coupled device (CCD) sensors (TLC sensors). Its dynamic exterior systematic errors (such as camera installation errors) and static interior distortion were eliminated using 1:2000 digital orthophotomaps and digital elevation models (DEMs) of the Dengfeng (Henan) and Tianjin areas of China as control data. Various CCD alignment calibration models were compared, on the basis of their geometric accuracy after calibration, to determine the optimal model. Finally, validation experiments were performed using ZY‐3 TLC images and ground control points (GCPs) collected over Anping in Hebei Province, Zhaodong in Heilongjiang Province and the Taihang Mountain area in China. The positioning accuracy attained its theoretical value over the Anping and Zhaodong areas. Using GCPs whose image coordinates were obtained manually, the plan and height accuracy were found to be better than 3 m and 2 m, respectively.

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