Image Mapping and Object Removal Using ADM in Image Inpainting: Review

Image inpainting is a technology for restoring the damaged parts of an image by referring to the information from the undamaged parts to make the restored image look “complete”, “continuous” and “natural”. Inpainting traditionally has been done by professional restorers. For instance, in the valuable painting such as in the museum world would be carried out by a skilled art conservator or art restorer. But this process is manual so it is time consuming. Digital Image Inpainting tries to imitate this process and perform the Inpainting automatically. The aim of this work is to develop an automatic system that can remove unwanted objects from the image and restore the image in undetectable way. Among various image inpainting algorithms Alternating Direction Method (ADM) is used for image restoration. ADM works well for solving inverse problem. In this paper, various applications of ADM method for image restoration are discussed. DOI: 10.17762/ijritcc2321-8169.15030

INTEGRATED LOW LIGHT IMAGE ENHANCEMENT IN TRANSPORTATION SYSTEM

Recent Intelligent Transportation System (ITS) focuses on both traffic management and Homeland Security. It involves advance detection systems of all kind but proper analysis of the image data is required for controlling and further processing. It becomes even more difficult when it comes to low light images due to limitation in the image sensor and heavy amount of noise. An ITS supports all levels like (Transport policy level, Traffic control tactical level, Traffic control measure level, Traffic control operation). For this it uses several split systems like Real time passenger information (RTPI), Automatic Number Plate Recognition (ANPR), Variable message signs (VMS), Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) system. While analyzing critical scenarios, mostly for the development of the application for Vehicle to Infrastructure (V2I) System several cases are taken into consideration. From these cases some are very difficult to analyze due to the visibility of the background as the detail structure is taken into consideration. Here Direct processing of low light images or video frames like day images leads to loss of required data, so an efficient enhancement method is required which gives allowable result for further transformation and analysis with minimal processing. So an Adaptive Enhancement Method is presented here which applies different enhancement methods for day light and low light images separately. For this purpose a combination of image fusion, edge detection filtering and Contourlet transformation is used for low light images; tone level adjustment and low level feature extraction for enhancement of day light images

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)

A hybrid alternating proximal method for blind video restoration

International audienceOld analog television sequences suffer from a number of degradations. Some of them can be modeled through convolution with a kernel and an additive noise term. In this work, we propose a new blind deconvolution algorithm for the restoration of such sequences based on a variational formulation of the problem. Our method accounts for motion between frames, while enforcing some level of temporal continuity through the use of a novel penalty function involving optical flow operators, in addition to an edge-preserving regularization. The optimization process is performed by a proximal alternating minimization scheme benefiting from theoretical convergence guarantees. Simulation results on synthetic and real video sequences confirm the effectiveness of our method

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)

Semi-blind Sparse Image Reconstruction with Application to MRFM

We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization (AM) algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.Comment: This work has been submitted to the IEEE Trans. Image Processing for possible publicatio

Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization

A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported