Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is built according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-based estimation technique in order to estimate the noise level in natural images and use it in blind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub-pixels in the tested images sing principal component analysis (PCA). The performance of the suggested noise level estimation technique is shown its superior to state of the art noise estimation and noise removal algorithms, the proposed algorithm produces the best performance in most cases compared with the investigated techniques in terms of PSNR, IQI and the visual perception

Mixed Noise Removal by Processing of Patches

Sonar images are degraded by mixed noise which has an adverse impact on detection and classification of underwater objects. Existing denoising methods of sonar images remove either additive noise or multiplicative noise. In this study, the mixed noise in sonar images, the additive Gaussian noise and the multiplicative speckle effect are handled by the data adaptive methods. A patch based denoising is applied in two phases to remove the additive Gaussian and multiplicative speckle noises. In the first phase, the adaptive processing of local patches is used to remove the additive Gaussian noise by exploiting the sonar image local sparsity. The PCA and SVD methods are used for denoising the noisy image patches and blocks of patches. In the second phase, the weighted maximum likelihood denoising of the nonlocal patches reduces the speckle effect by exploiting the non-local similarity in a probability distribution. Experiments on side scan sonar images are conducted and the results show the importance of removing both the additive and multiplicative components from the sonar images

Detection and estimation of image blur

The airborne imagery consisting of infrared (IR) and multispectral (MSI) images collected in 2009 under airborne mine and minefield detection program by Night Vision and Electronic Sensors Directorate (NVESD) was found to be severely blurred due to relative motion between the camera and the object and some of them with defocus blurs due to various reasons. Automated detection of blur due to motion and defocus blurs and the estimation of blur like point spread function for severely degraded images is an important task for processing and detection in such airborne imagery. Although several full reference and reduced reference methods are available in the literature, using no reference methods are desirable because there was no information of the degradation function and the original image data. In this thesis, three no reference algorithms viz. Haar wavelet (HAAR), modified Haar using singular value decomposition (SVD), and intentional blurring pixel difference (IBD) for blur detection are compared and their performance is qualified based on missed detections and false alarms. Three human subjects were chosen to perform subjective testing on randomly selected data sets and the truth for each frame was obtained from majority voting. The modified Haar algorithm (SVD) resulted in the least number of missed detections and least number of false alarms. This thesis also evaluates several methods for estimating the point spread function (PSF) of these degraded images. The Auto-correlation function (ACF), Hough transform (Hough) and steer Gaussian filter (SGF) based methods were tested on several synthetically motion blurred images and further validated on naturally blurred images. Statistics of pixel error estimate using these methods were computed based on 8640 artificially blurred image frames --Abstract, page iii

Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition

This paper deals with the rotation synchronization problem, which arises in global registration of 3D point-sets and in structure from motion. The problem is formulated in an unprecedented way as a "low-rank and sparse" matrix decomposition that handles both outliers and missing data. A minimization strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against state-of-the-art algorithms on simulated and real data. The results show that R-GoDec is the fastest among the robust algorithms.Comment: The material contained in this paper is part of a manuscript submitted to CVI