Restoration of blurred images using Blind Deconvolution Algorithm

Image restoration is the process of recovering the original image from the degraded or noisy image. Blind Deconvolution Algorithm can be used where no information about PSF value, it restores image and PSF simultaneously. There are multiple noisy images for deblurring. The first task is original images are degraded by degradation model. It can be done by Gaussian Filter which is low-pass filter. The second task is canny edge detection method is used for identifying the strong and weak edge. The final task is deblured images is recovered by parallel computing task and restored at a time. Once the deblur kernel is estimated using our method. We use this parallel computing approach for the multiple images. A local scheduler done the parallel work. We focus our attention on the problem of restoring multiple images corrupted by blur and noise.

Laser pointing systems for small targets are mainly confronted with two pointing errors, jitter and boresight, arising due to atmospheric turbulence, mechanical vibrations and errors in optical alignment. Knowledge of these parameters provides information about the quality of the pointing and tracking system. In the past, several techniques have been investigated to estimate these parameters from returned laser signals. These include the key ratio technique, the chi-squared method and the maximum likelihood (ML) estimation technique. These techniques have been studied in the literature for Gaussian irradiance profiles. In particular, the ML estimation technique has been found to provide excellent results. In this paper, we extend the ML estimation technique from Gaussian profiles to near-Gaussian irradiance profiles. Our results show that the modified estimator performs much better than a direct application of the original ML estimator.

In this new information age, high data rate and strong reliability features our wireless communication systems and is becoming the dominant factor for a successful deployment of commercial networks. MIMO-OFDM (multiple input multiple output-orthogonal frequency division multiplexing), a new wireless broadband technology, has gained great popularity for its capability of high rate transmission and its robustness against multi-path fading and other channel impairments. A major challenge to MIMO-OFDM systems is how to obtain the channel state information accurately and promptly for coherent detection of information symbols and channel synchronization. In the first part, this dissertation formulates the channel estimation problem for MIMO-OFDM systems and proposes a pilot-tone based estimation algorithm. A complex equivalent base-band MIMO-OFDM signal model is presented by matrix representation. By choosing equally-spaced and equally-powered pilot tones from sub-carriers in one OFDM symbol, a down-sampled version of the original signal model is obtained. Furthermore, this signal model is transformed into a linear form solvable for the LS (least-square) estimation algorithm. Based on the resultant model, a simple pilot-tone design is proposed in the form of a unitary matrix, whose rows stand for different pilot-tone sets in the frequency domain and whose columns represent distinct transmit antennas in the spatial domain. From the analysis and synthesis of the pilot-tone design in this dissertation, our estimation algorithm can reduce the computational complexity inherited in MIMO systems by the fact that the pilot-tone matrix is essentially a unitary matrix, and is proven an optimal channel estimator in the sense of achieving the minimum MSE (mean squared error) of channel estimation for a fixed power of pilot tones. In the second part, this dissertation addresses the wireless location problem in WiMax (worldwide interoperability for microwave access) networks, which is mainly based on the MIMO-OFDM technology. From the measurement data of TDOA (time difference of arrival), AOA (angle of arrival) or a combination of those two, a quasi-linear form is formulated for an LS-type solution. It is assumed that the observation data is corrupted by a zero-mean AWGN (additive white Gaussian noise) with a very small variance. Under this assumption, the noise term in the quasi-liner form is proved to hold a normal distribution approximately. Hence the ML (maximum-likelihood) estimation and the LS-type solution are equivalent. But the ML estimation technique is not feasible here due to its computational complexity and the possible nonexistence of the optimal solution. Our proposed method is capable

Edge detection is often used in image processing to find the edge boundaries of image objects and this is important for selecting the edges of image parts. The image edge detection used in this study counts coins in an image, so that it can easily pass through the human eye but not by a computer. In a computer system, it is necessary to provide programs and test data so that the computer can read and count coins like a human. The purpose of this study is to count coins in an image using Canny edge detection and Gaussian filtering methods. The images used in this study are in the form of *jpg and tested using the Visual Studio Code application using OpenCV and Python. The results of this study indicate that the results of the number of coins read from the images inputted to the system can be read properly, with a data accuracy of 90%. Canny's edge detection method can be used to count the number of coins in an image.Keywords: Pengolahan Citra; Deteksi tepi Canny; Gaussian filtering; OpenCV;

Quantization-based watermarking schemes comprise a class of watermarking schemes that achieves the channel capacity in terms of additive noise attacks. The existence of good high dimensional lattices that can be efficiently implemented and incorporated into watermarking structures, made quantization-based watermarking schemes of practical interest. Because of the structure of the lattices, watermarking schemes making use of them are vulnerable to non-additive operations, like amplitude scaling in combination with additive noise. In this paper, we propose a secure Maximum Likelihood (ML) estimation technique for amplitude scaling factors using subtractive dither. The dither has mainly security purposes and is assumed to be known to the watermark encoder and decoder. We derive the probability density function (PDF) models of the watermarked and attacked data in the presence of subtractive dither. The derivation of these models follows the lines of reference 5, where we derived the PDF models in the absence of dither. We derive conditions for the dither sequence statistics such that a given security level is achieved using the error probability of the watermarking system as objective function. Based on these conditions we are able to make approximations to the PDF models that are used in the ML estimation procedure. Finally, experiments are performed with real audio and speech signals showing the good performance of the proposed estimation technique under realistic conditions.

In this paper, a novel blind digital image watermarking scheme in contour let domain with singular value decomposition is proposed. In contrast to traditional methods where the watermark bits are embedded directly on the contourlet transform coefficients, the proposed scheme is based on watermark bits embedding on the singular value of the selected blocks within lowpass subband of the original gray image contourlet transform. Experimental results demonstrate that the quality of watermarked image is robust against attacks such as JPEG compression, low pass filtering, noise addition, scaling and cropping. Watermark extraction is efficient and blind in the sense only quantization strategies but not the original image is required.

Images encoded at low bit rate may suffer from blocking artifacts, which dramatically degrade the visual quality. In this paper, we propose a postprocessing image restoration scheme for JPEG compressed images based on the maximum a posteriori criterion. A degradation model, represent by additive Gaussian noise model, is proposed to simulate JPEG compression process, while the original image is modeled as a high order Markov random field (MRF) based on the fields of experts framework. Meanwhile, an enhancement algorithm is proposed to restore the high frequency (HF) components. Experiment results have demonstrated that the proposed scheme can reproduce higher-quality images in terms of both objective and subjective quality.

Dependent component analysis for blind restoration of images degraded by turbulent atmosphere

In cases in which an original image is blind, a decoding method where both the image and the messages can be estimated simultaneously is desirable. We propose a spread spectrum watermarking model with image restoration based on Bayes estimation. We therefore need to assume some prior probabilities. The probability for estimating the messages is given by the uniform distribution, and the ones for the image are given by the infinite-range model and two-dimensional (2D) Ising model. Any attacks from unauthorized users can be represented by channel models. We can obtain the estimated messages and image by maximizing the posterior probability. We analyzed the performance of the proposed method by the replica method in the case of the infinite-range model. We first calculated the theoretical values of the bit error rate from obtained saddle-point equations and then verified them by computer simulations. For this purpose, we assumed that the image is binary and is generated from a given prior probability. We also assume that attacks can be represented by the Gaussian channel. The computer simulation retults agreed with the theoretical values. In the case of prior probability given by the 2D Ising model, in which each pixel is statically connected with four-neighbors, we evaluated the decoding performance by computer simulations, since the replica theory could not be applied. Results using the 2D Ising model showed that the proposed method with image restoration is as effective as the infinite-range model for decoding messages. We compared the performances in a case in which the image was blind and one in which it was informed. The difference between these cases was small as long as the embedding and attack rates were small. This demonstrates that the proposed method with simultaneous estimation is effective as a watermarking decoder.

Image restoration and its different variations are important topics in low-level image processing. One of the main challenges in image restoration is dependency of current methods to the corruption characteristics. In this paper, we have proposed an image restoration architecture that enables us to address different types of corruption, regardless of type, amount and location. The main intuition behind our approach is restoring original images from abstracted perceptual features. Using an encoder-decoder architecture, image restoration can be defined as an image transformation task. Abstraction of perceptual features is done in the encoder part of the model and determines the sampling point within original images' Probability Density Function (PDF). The PDF of original images is learned in the decoder section by using a Generative Adversarial Network (GAN) that receives the sampling point from the encoder part. Concretely, sampling from the learned PDF restores original image from its corrupted version. Pretrained network extracts perceptual features and Restricted Boltzmann Machine (RBM) makes the abstraction over them in the encoder section. By developing a new algorithm for training the RBM, the features of the corrupted images have been refined. In the decoder, the Generator network restores original images from abstracted perceptual features while Discriminator determines how good the restoration result is. The proposed approach has been compared with both traditional approaches like BM3D and with modern deep models like IRCNN and NCSR. We have also considered three different categories of corruption including denoising, inpainting and deblurring. Experimental results confirm performance of the model.

Abstract: Abstract: this study evaluates and compares the performance of two edge detection methods Sobel method and Canny method on batik image. Batik images have unique characteristics and complex patterns, making it difficult to analyze the edges. This study presents a comparison of the results using sobel and canny edge detection methods on batik kawung images both from peak signal-to-noise reatio and from mean squared error. The results showed that canny edge detection was better than sobel method. This can be seen from the results of PSNR and MSE that is 100%. This analysis is determined by considering factors such as the accuracy of edge detection, sensitivity to noise, and the ability to handle the complexity of batik drawing patterns. The results of this study provide a detailed description of the advantages and disadvantages of each method in the image of batik kawung. The conclusions that can be drawn from this study can provide valuable guidance for choosing the optimal edge detection method in image analysis of batik kawung and others. Keywords: batik kawung; canny; MSE; PSNR; sobel Abstrak: Penelitian ini mengevaluasi dan membandingkan kinerja dua metode deteksi tepi metode Sobel dan metode Canny pada citra batik. Gambar batik mempunyai ciri-ciri yang unik dan pola yang kompleks, sehingga menyulitkan analisis tepian. Penelitian ini menyajikan perbandingan hasil menggunakan metode deteksi tepi sobel dan canny pada citra batik kawung baik dari peak signal-to-noise reatio maupun dari mean squared error. Hasil penelitian ini menunjukkan bahwa deteksi tepi canny lebih baik dibandingkan dari metode sobel. Hal ini dapat dilihat dari hasil PSNR dan MSE yang dihasilkan yaitu 100%. Analisis ini ditentukan dengan mempertimbangkan faktor-faktor seperti keakuratan deteksi tepi, kepekaan terhadap noise, dan kemampuan menangani kompleksitas pola gambar batik. Hasil penelitian ini memberikan gambaran secara detail mengenai kelebihan dan kekurangan masing-masing metode pada citra batik kawung. Kesimpulan yang dapat diambil dari penelitian ini dapat memberikan panduan berharga untuk memilih metode deteksi tepi yang optimal dalam analisis citra batik kawung dan yang lainnya. Kata kunci: batik kawung; canny; MSE; PSNR; sobel

In this paper we present a theory for obtaining densities that are important for computer vision. As a result of the theory we compute the exact and novel density of the slope of a line fitted to image points. This density makes it possible to obtain confidence intervals for the slope or to make hypothesis testing about if two intersecting lines form a corner or not. The theory also lets us derive a novel technique for maximum likelihood estimation, that can be used for computing the fundamental matrix, conics, or any other constraint that can be expressed by polynomials of degree 2. We present exact and novel densities for the fundamental matrix and conic constraints, that are needed for the estimation. Experiments show how the results can be used in practise to compute maximum likelihood estimates of the fundamental matrix.

We develop and implement a technique for maximum likelihood estimation in closed-form of multivariate affine yield models of the term structure of interest rates. We derive closed-form approximations to the likelihood functions for all nine of the Dai and Singleton (2000) canonical affine models with one, two, or three underlying factors. Monte Carlo simulations reveal that this technique very accurately approximates true maximum likelihood, which is, in general, infeasible for affine models. We also apply the method to a dataset consisting of synthetic US Treasury strips, and find parameter estimates for nine different affine yield models, each using two different market price of risk specifications. One advantage of maximum likelihood estimation is the ability to compare non-nested models using likelihood ratio tests. We find, using these tests, that the choice of preferred canonical model depends on the market price of risk specification. Comparison to other approximation methods, Euler and QML, on both simulated and real data suggest that our approximation technique is much closer to true MLE than alternative methods.

Many image restoration processors based on convolutional neural network (CNN) has been proposed because of its high performance. However, it is well known that restoration by these networks is not robust against perturbations on a degradation model. If restoration fails, it is difficult for users to find the cause because no explanation is given by the network. In this paper, we propose an additional network to extract internal parameters for an image restoration network to supply users explaining information on restoration process. Experimental results show that the proposed network successfully extracts estimated degradation attributes and gives helpful information to assist users to find a root cause of a restoration failure.

<abstract><p>Analysis of the accuracy of estimated parameters is an important research direction. In the article, the maximum likelihood estimation is used to estimate CMOS image noise parameters and Fisher information is used to analyse their accuracy. The accuracies of the two parameters are different in different situations. Two applications of it are proposed in this paper. The first one is a guide to image representation. The standard pixel image has higher accuracy for signal-dependent noise and higher error for additive noise, in contrast to the normalised pixel image. Therefore, the corresponding image representation is chosen to estimate the noise parameters according to the dominant noise. The second application of the conclusions is a guide to algorithm design. For standard pixel images, the error of additive noise estimation will largely affect the final denoising result if two kinds of noise are removed simultaneously. Therefore, a divide-and-conquer hybrid total least squares algorithm is proposed for CMOS image restoration. After estimating the parameters, the total least square algorithm is first used to remove the signal-dependent noise of the image. Then, the additive noise parameters of the processed image are updated by using the principal component analysis algorithm, and the additive noise in the image is removed by BM3D. Experiments show that this hybrid method can effectively avoid the problems caused by the inconsistent precision of the two kinds of noise parameters. Compared with the state-of-art methods, the new method shows certain advantages in subjective visual quality and objective image restoration indicators.</p></abstract>