Image and video restoration

Top PDF Image and video restoration:

Information Theory Oriented Image Restoration

Information Theory Oriented Image Restoration

1.2 Contributions of the thesis The main contribution of this thesis is a new statistical framework inspired from Information Theory to address the problem of image restoration. Many problems of image and video processing can be expressed as the minimization of a data con- sistency residual and a term of mismatch with respect to a priori constraints. Tra- ditionally, these functionals are based on penalization functions such as the ones defined for robust estimation, sometimes referred to as φ-functions. From a statis- tical point of view, recurring to these functions is equivalent to implicitly making assumptions on the probability density functions (PDFs) of the residual and the model mismatch, e.g., Gaussian, Laplacian, or other parametric laws for the square function, the absolute value, or other φ-functions, respectively. Alternatively, it is interesting to adapt to (an estimation of) the true PDF. This nonparametric ap- proach implies to define functionals which take PDFs as input. Entropy has been proposed in this context since, as a measure of dispersion of a PDF, its minimiza- tion leads the residual or model mismatch values to concentrate around narrow modes, the highest one normally corresponding to the annihilation of the residual or mismatch, the others corresponding to inevitable outliers.
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A Multicore Convex Optimization Algorithm with Applications to Video Restoration

A Multicore Convex Optimization Algorithm with Applications to Video Restoration

Numerous tasks in image processing, such as video restora- tion, can be formulated as nonsmooth optimization problems over large datasets. In this context, it is necessary to pro- pose parallel/distributed methods to compute efficiently the solutions to the corresponding high-dimensional optimiza- tion problems. In this work, we focus on the case when the objective function is a sum of several convex non-necessarily smooth functions [1]. In the general case, a closed form expression of the solution does not exist, and developing iter- ative strategies becomes necessary.

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Bayesian image restoration for mosaic active imaging

Bayesian image restoration for mosaic active imaging

Generally, the eld of view of the camera is fully illuminated by the laser and is acquired at standard video rates, say 10 Hz. In mosaic laser imaging, we re- place the low-repetition-rate 10Hz laser with optical parametric oscillator by a high-repetition-rate 10kHz ber laser. The latter is expected to oer higher average power and plug-eciencies within a few years. This concept presents additional advantages. As the repetition rate is larger by three orders of magnitude, the en- ergy per pulse is lowered by the same ratio. In order to maintain the signal-to-noise ratio, only a reduced part of the eld of view is illuminated at each laser ash. The corresponding region of interest of the sensor is read. The laser beam is then deected in order to illuminate another region of interest. By repeating the process, we scan the eld of view of the camera. This results in the successive acquisition of elementary images taken at a repetition-rate of 10 kHz that will tile as a mosaic in order to build the full-frame image at 10 Hz. The formation of each elementary image can be modeled as follows.
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Nonlocal Mumford-Shah Regularizers for Color Image Restoration

Nonlocal Mumford-Shah Regularizers for Color Image Restoration

Image inpainting, also known as image interpolation, is the process of reconstructing lost or corrupted parts of an image, that is an interesting and important inverse problem with many applications such as removal of scratches in old photos, removal of overlaid text or graphics, filling-in missing blocks in unreliably transmitted images. Non- texture image inpainting has received considerable interest since the pioneering paper by Masnou and Morel [54], [55] who proposed variational principles for image disocclusion. A recent wave of interest in inpainting has also started from [11], where applications in the movie industry, video and art restoration were unified. These authors proposed nonlinear partial differential equations for non-texture inpainting. Moreover, many contributed works have been proposed for the solution of this interpolation task based on (a) diffusion and transport PDE/variational principle [23], [13], [24], [26], [25], [35], [68], [63], [4], (b) exemplar region fill-in [32], [75], [12], [31], [61], [29], (c) compressive sensing [33], [70]. Inpainting corresponds to the operation H of losing pixels from an image, i.e. the observed data f is given by
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Restoration super-resolution of image sequences : application to TV archive documents

Restoration super-resolution of image sequences : application to TV archive documents

represents additive noise. Video deblurring is highly related to multi-image deconvolution [ Paragios et al. , 2006 ; Chen et al. , 2008 ; Cai et al. , 2009 ; Sroubek and Milanfar , 2012 ]. It has been shown in [ Cai et al. , 2009 ] that given multiple observations, enforcing the frame sparsity improves the accuracy of identifying the blur kernels and reduces the ill- posedness of the problem. However, multi-image deconvolution algorithms require that all the input images are aligned and that the content is the same (static scene). On the other hand, the authors in [ Li et al. , 2010 ] proposed to estimate the camera motion and to explicitly model the video blur as a function of the motion being esti- mated. A joint energy function is formulated between the underlying sharp sequence and motion parameters. Recently, [ Kim and Lee , 2015 ] proposed to simultaneously tackle the problem of optical flow estimation and frame restoration in general blurred videos. This is done by simultaneously estimating the optical flow and latent sharp frames through the minimization of a single nonconvex energy function. Addressing these two problems simultaneously requires a much more complex optimization, due to the more sophisticated direct model linking all the blurry observations.
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Towards Perceptually Plausible Training of Image Restoration Neural Networks

Towards Perceptually Plausible Training of Image Restoration Neural Networks

Nantes, France patrick.lecallet@univ-nantes.fr Abstract—Learning-based black-box approaches have proven to be successful at several tasks in image and video processing domain. Many of these approaches depend on gradient-descent and back-propagation algorithms which requires to calculate the gradient of the loss function. However, many of the visual metrics are not differentiable, and despite their superior accuracy, they cannot be used to train neural networks for imaging tasks. Most of the image restoration neural networks rely on mean squared error to train. In this paper, we investigate visual system based metrics in order to provide perceptual loss functions that can replace mean squared error for gradient descent- based algorithms. We also share our preliminary results on the proposed approach.
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Validation of image restoration methods on 3D-printed ultrasound phantoms

Validation of image restoration methods on 3D-printed ultrasound phantoms

III. R ESULTS AND D ISCUSSION A. Phantom quality and reproducibility The reproducibility of phantom printing can refer to reproducibility between or within phantoms. While accurate scatterer positioning could be achieved with a judicious choice of printing parameters, it was found that even using the same printer settings, some printed phantoms showed contamination of the propagation medium with scattering material. The reason behind this merits further investigation. Although scatterer diameters as low as 50 μm could be printed, setting it to 100 μm (the setting presented in the current work) substantially reduced variations in scatterer diameter. As regards variations of scatterer printing within phantoms, Fig. 2 shows the B-mode image of a successfully printed typical phantom. It can be observed that the lateral variation of the scatterer responses around the outer frame is relatively small compared to the axial variation. This suggests that the scatterer diameters are fairly reproducible, with the relatively high amplitude of the response at 20 mm hypothesized to be due to elevational focusing of the transducer. As judged by the location of the outer ring of scatterers, the scatterer placement is also accurate.
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Validation of image restoration methods on 3D-printed ultrasound phantoms

Validation of image restoration methods on 3D-printed ultrasound phantoms

Knowledge of the scattering function allows comparison of the deconvolved images with the ground truth. Thus, using the scattering function and the originally acquired B-mode image, performance of image restoration methods could be evaluated quantitatively through comparison of root mean square error and full width half maximum values. Preliminary results demonstrate the benefits of knowing the scattering function during experimental testing of image restoration methods. In summary, the current work shows the potential of an experimental method for evaluating the extent to which an image restoration method provides a faithful rendering of the underlying scattering structure.
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Simultaneous Image Restoration and Hyperparameter Estimation for Incomplete Data by a Cumulant Analysis

Simultaneous Image Restoration and Hyperparameter Estimation for Incomplete Data by a Cumulant Analysis

Projet PASTIS Rapport de recherche n˚3249 — September 1997 — 68 pages Abstract: The purpose of this report is first to show the main properties of Gibbs distributions considered as exponential statistics on finite spaces, as well as their sampling and annealing properties. Moreover, the definition and use of their cu- mulant expansions enables to exhibit other important properties of such distribu- tions. Last, we tackle the problem of hyperparameter estimation in an incomplete data frame for image restoration purposes. A detailed analysis of several joint restoration-estimation methods using generalized stochastic gradient algorithms is presented, requiring infinite, continuous configuration spaces. Using once again cumulant analysis and its relationship with Statistical Physics allows us to propose new algorithms and to extend them to an explicit boundary frame.
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Priority image and video encoding transmission based on a discrete Radon transform

Priority image and video encoding transmission based on a discrete Radon transform

I. I NTRODUCTION Part 11 of the JPEG2000 standard extends it beyond the scope of image compression toward a global wire- less transmission architecture. The JPEG2000 Wireless (JPWL) transmission consists of the core coding system and an Unequal Error Protection (UEP) scheme driven by semantic information reflecting the error sensitivity of each part of the bitstream (Fig. 1). An emphasized protection of the image and tiles headers has been pro- posed [1] because errors occurring at these levels have a dramatic impact on the overall image quality. References [2], [3], [4] have shown the efficiency of UEP schemes over traditional Equal Error Protection (EEP) schemes for multimedia content. Typically, Reed-Solomon (RS) codes are used as Forward Error Correction (FEC) codes. In [5], the RS codes (160,64), (80,25) and (40,13) are used. They introduce redundancy ratios of 1.5, 2.2 and 2.08, respectively. This robust protection improves significantly the probability of successful decoding independently of the channel conditions because the integrity of the headers is preserved in case of binary losses.
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Image Sequence Restoration: A PDE Based Coupled Method for Image Restoration and Motion Segmentation

Image Sequence Restoration: A PDE Based Coupled Method for Image Restoration and Motion Segmentation

101 - 54602 Villers lès Nancy Cedex France Unité de recherche INRIA Rennes : IRISA, Campus universitaire de Beaulieu - 35042 Rennes Cedex France Unité de recherche INRIA Rhône-Alpes : 65[r]

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Multidimensional Hidden Markov Model Applied to Image and Video Analysis

Multidimensional Hidden Markov Model Applied to Image and Video Analysis

La classification d’images constitue sans doute, la partie la plus importante de l’analyse de l’image numérique. L’objectif est d’identifier et de décrire les caractéris- tiques présentes dans une image afin de les répertorier par classes et par thèmes. Des applications existent dans un grand nombre de domaines, tels que l’interprétation de l’imagerie médicale, la surveillance, la photo satellite et la télévision interactive. Les méthodes traditionnelles de classification d’images procèdent par analyse des blocs distincts d’une image, ce qui aboutit à un formalisme non contextuel des caractéristiques visuelles. Toutefois, face à l’analyse d’une parcelle d’image, l’œil humain est souvent dans l’incapacité d’identifier ce qu’il voit. Les approches récen- tes tendent donc de plus en plus vers une vision globale de l’image incluant sa structure et sa forme générale (ex: le soleil dans le ciel, le ciel au dessus d’un paysage ou encore un bateau sur l’eau, etc.) [20], [21].
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Light-field image and video compression for future immersive applications

Light-field image and video compression for future immersive applications

Integral imaging cannot be used for applications where a large angle of view is required, such as Free Navigation for example, as it only captures the light-field under a narrow angle of view. In Part III, we also study the compression of Super Multi-View content, that provides a sparser sampling of the light-field but with a large baseline. In Chapter 4, we present a subjective quality evaluation of compressed Super Multi-View content on a light- field display system. The goal is to study the impact of compression at the display side in the specific case of light-field content. We provide some initial conclusions on the feasibility of a video service that would require rendering about 80 views. We first show that the bitrates required for encoding and rendering 80 views are realistic and coherent with future networks requirements to support 4K/8K, although some considerations on the tested content characteristics highlight the need for a better codec, in order to further improve the quality and avoid network overload. Preliminary experiments performed during this study lead to recommended coding configurations for Super Multi-View video content, particularly with groups of views (GOVs), that enable a compromise between memory limitations, coding efficiency and parallel processing. Some conclusions are also drawn on the amount of views to skip at the encoder, and to synthesize after the decoding, that is highly sequence-dependent. The ratio between coded and synthesized views depends on the quality of the synthesized views, hence is linked to the quality of the depth maps, the efficiency of the renderer, and the complexity of the scene. Apart from compression, view synthesis can introduce severe distortions, and affects the overall rendering scheme. Our results confirm that improvement of view synthesis and depth estimation algorithms is mandatory. Concerning the evaluation method and metric, results show that the PSNR remains able to reflect an increase or decrease in subjective quality for light-field content. However, depending on the ratio of coded and synthesized views, we have observed that the order of magnitude of the effective quality variation is biased by the PSNR, that is less tolerant to view synthesis artifacts than human viewers.
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Fast image and video segmentation based on alpha-tree multiscale representation

Fast image and video segmentation based on alpha-tree multiscale representation

Recently, a new image model, namely the α-tree [1], has been introduced as a powerful tool for multiscale image representation. It offers a compact and efficient way to access image content, and can be further exploited in various image analysis and processing tasks. We consider here the α-tree for image segmentation purpose, and study how this new image model can be used in such a context. Indeed, some relevant image features can be extracted from the tree, leading then in segmentation methods operating either auto- matically or interactively. Moreover, we propose an efficient implementation scheme which ensures user interactivity and extension to video data. Preliminary results obtained on the Berkeley Segmentation Dataset are very promising and show the relevance of the α-tree in image processing and analysis. This paper is organized as follows. In the next section, we recall the definitions of flat and quasi-flat zones, that lead to the α-tree model for image representation. We then describe our contribution in Sec. III where we study how the α-tree can provide relevant features for image segmentation before introducing a new segmentation method and its efficient implementation. In Sec. IV, we discuss parameter settings and provide an experimental evaluation of our method on the Berkeley Segmentation Dataset. We also provide an insight
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Image and video quality assessment using LCD: comparisons with CRT conditions

Image and video quality assessment using LCD: comparisons with CRT conditions

been set in such a way that one pixel on the display array is pictured by 4×4 pixels on the CCD array. This permitted us to obtain a good approximation of the 56×40 pixels display frame by computing the mean of each 4×4 blocks in the CCD frame. Stim- uli were generated with Matlab on a PC using the PsychToolbox extension [22]. They consisted of a straight edge moving from left to right. One exam- ple of frames grabbed by the CCD camera is shown in Figure 6. As mentioned before, the blurred pro- file was obtained by motion compensation of each CCD frames to simulate the smooth pursuit of the eyes. The high camera frame rate and the precise calibration of apparatus to have 4×4 CCD cam- era pixels to picture one display pixel permit us to achieve this motion compensation precisely. Next, all frames are added to each other to simulate the temporal integration on the retina. An example of blurred edge obtained with this method is shown in Figure 7 for a edge moving with a velocity V = 10 pixels per frame. The blurred edge width BEW (in pixels) is measured as illustrated. The blurred edge time BET (in seconds or in frames) is generally used, it’s expressed by dividing BEW by the ve- locity V (in pixels per seconds or pixels per frame): BET = BEW/V (3) Moreover, it has been observed that for a given grey-to-grey transition (i.e. for a given temporal re- sponse of the liquid crystal cells), BET was not varying with the velocity V . In other terms, the measured blur width BEW was proportional to the velocity of the moving edge. This result agree with the relation 2 and the parameter a can then be identified with the blurred edge time BET .
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Learning visual representations with neural networks for video captioning and image generation

Learning visual representations with neural networks for video captioning and image generation

given N training pairs (w (n) , a (n,k) ). θ represents parameters in the LSTM. 7.3.2. Atoms Construction Each configuration of a may be associated with a different distribution P θ (w|a), therefore a different oracle model. We define configuration as an orderless collection of unique atoms. That is, a (k) = {a 1 , . . . , a k } where k is the size of the bag and all items in the bag are different from each other. Considering the particular problem of image and video captioning, atoms are defined as words in captions that are most related to actions, entities, and attributes of entities (in Figure 7.1 ). The reason of using these three particular choices of language com- ponents as atoms is not an arbitrary decision. It is reasonable to consider these three types among the most visually perceivable ones when human describes visual content in natural language. We further verify this by conducting a human evaluation procedure to identify “visual” atoms from this set and show that a dominant majority of them indeed match human visual perception, detailed in Section 7.5.1 . Being able to capture these important concepts is considered as crucial in getting superior performance. Therefore, comparing the performance of existing models against this oracle reveals their ability of capturing atoms from visual inputs when P (a|v) is unknown.
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Image restoration using a kNN-variant of the mean-shift

Image restoration using a kNN-variant of the mean-shift

The k nearest neighbors are provided by the Approximate Nearest Neighbor Searching (ANN) library (available at http://www.cs.umd.edu/˜mount/ANN/ ). In order to measure the performance of our algorithm we degraded the Lena image (256x256) adding a gaussian noise with standard deviation σ = 10. The original image has in- tensity ranging from 0 to 100. We consider a 9 x 9 neighbor- hoods, and we add spatial features to the original radiometric data [10, 13], as explained in section 2. These spatial features allow us to reduce the effect of the non stationarity of the sig- nal in the estimation process, by preferring regions closer to the estimation point. The dimension of the data d is therefore equal to 83, and we have to search the k nearest neighbors in such a high dimensional space.
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Stochastic forward-backward and primal-dual approximation algorithms with application to online image restoration

Stochastic forward-backward and primal-dual approximation algorithms with application to online image restoration

Stochastic approximation techniques have been used in var- ious contexts in data science. We propose a stochastic ver- sion of the forward-backward algorithm for minimizing the sum of two convex functions, one of which is not necessarily smooth. Our framework can handle stochastic approxima- tions of the gradient of the smooth function and allows for stochastic errors in the evaluation of the proximity operator of the nonsmooth function. The almost sure convergence of the iterates generated by the algorithm to a minimizer is es- tablished under relatively mild assumptions. We also propose a stochastic version of a popular primal-dual proximal split- ting algorithm, establish its convergence, and apply it to an online image restoration problem.
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Fast Visibility Restoration from a Single Color or Gray Level Image

Fast Visibility Restoration from a Single Color or Gray Level Image

tarel@lcpc.fr hautiere@lcpc.fr Abstract One source of difficulties when processing outdoor im- ages is the presence of haze, fog or smoke which fades the colors and reduces the contrast of the observed objects. We introduce a novel algorithm and variants for visibility restoration from a single image. The main advantage of the proposed algorithm compared with other is its speed: its complexity is a linear function of the number of image pixels only. This speed allows visibility restoration to be applied for the first time within real-time processing appli- cations such as sign, lane-marking and obstacle detection from an in-vehicle camera. Another advantage is the pos- sibility to handle both color images or gray level images since the ambiguity between the presence of fog and the ob- jects with low color saturation is solved by assuming only small objects can have colors with low saturation. The al- gorithm is controlled only by a few parameters and con- sists in: atmospheric veil inference, image restoration and smoothing, tone mapping. A comparative study and quanti- tative evaluation is proposed with a few other state of the art algorithms which demonstrates that similar or better qual- ity results are obtained. Finally, an application is presented to lane-marking extraction in gray level images, illustrating the interest of the approach.
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A review of deep-learning techniques for SAR image restoration

A review of deep-learning techniques for SAR image restoration

3.2. Extensions to polarimetric and/or interferometric SAR Most deep learning approaches for speckle reduction focused on the case of intensity images. Multi-channel complex- valued SAR images, as in SAR polarimetry or in SAR in- terferometry, raise other challenges. Polarimetric and in- terferometric information are encoded in complex-valued covariance matrices. Restricting the estimated matrices to the cone of positive definite covariance matrices requires an ad- equate design of the learning strategy and/or of the network. Due to the increase of the dimensionality of the data and of the unknowns, the learning task becomes more complex and it is expected that many more training samples are re- quired to capture all spatial and polarimetric/interferometric configurations during the learning phase.
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