Image noise is unwanted fluctuations in pixel intensities that is often inevitable during the process of acquisition, compression, and transmission for many reasons such as imperfections in capturing instruments, limitations of analog-to-digital converters, and interference in transmission channels. The existence of noise not only degrades the visual quality of images but also adversely affects the performance of image processing and computer vision tasks, such as classification, detection, and segmentation. Thus, removing or attenuating the effect of image noise (i.e., image denoising) is often an essential preprocessing task in the field of image processing and computer vision. In this dissertation, we addressed three image noises: salt-and-pepper (SAP) impulse noise, random-valued impulse noise (RVIN), and Gaussian noise. In order to restore images contaminated by SAP impulse noise, we used the modified mean filter and total variation of corrupted pixels that was minimized by using convex optimization. For RVIN, we implemented a three-step method that restored images by estimating the noise density, detecting the noisy pixels, and applying a modified weighted mean filter to the detected noisy pixels. For images corrupted by Gaussian noise, we proposed a deep convolutional neural network that handled a wide range of noise levels by using two trained models, one for low noise and the other for high noise levels. We compared the performance of our noise removal methods with other state-of-the-art algorithms and in the vast majority of the cases, our method outperformed other image denoising algorithms which showed the effectiveness of the proposed methods.
Presents a review of image denoising algorithms with practical MATLAB implementation guidance Digital Image Denoising in MATLAB provides a comprehensive treatment of digital image denoising, containing a variety of techniques with applications in high-quality photo enhancement as well as multi-dimensional signal processing problems such as array signal processing, radar signal estimation and detection, and more. Offering systematic guidance on image denoising in theories and in practice through MATLAB, this hands-on guide includes practical examples, chapter summaries, analytical and programming problems, computer simulations, and source codes for all algorithms discussed in the book. The book explains denoising algorithms including linear and nonlinear filtering, Wiener filtering, spatially adaptive and multi-channel processing, transform and wavelet domains processing, singular value decomposition, and various low variance optimization and low rank processing techniques. Throughout the text, the authors address the theory, analysis, and implementation of the denoising algorithms to help readers solve their image processing problems and develop their own solutions. Explains how the quality of an image can be quantified in MATLAB Discusses what constitutes a “naturally looking” image in subjective and analytical terms Presents denoising techniques for a wide range of digital image processing applications Describes the use of denoising as a pre-processing tool for various signal processing applications or big data analysis Requires only a fundamental knowledge of digital signal processing Includes access to a companion website with source codes, exercises, and additional resources Digital Image Denoising in MATLAB is an excellent textbook for undergraduate courses in digital image processing, recognition, and statistical signal processing, and a highly useful reference for researchers and engineers working with digital images, digital video, and other applications requiring denoising techniques.
This unique text/reference presents a detailed review of noise removal for photographs and video. An international selection of expert contributors provide their insights into the fundamental challenges that remain in the field of denoising, examining how to properly model noise in real scenarios, how to tailor denoising algorithms to these models, and how to evaluate the results in a way that is consistent with perceived image quality. The book offers comprehensive coverage from problem formulation to the evaluation of denoising methods, from historical perspectives to state-of-the-art algorithms, and from fast real-time techniques that can be implemented in-camera to powerful and computationally intensive methods for off-line processing. Topics and features: Describes the basic methods for the analysis of signal-dependent and correlated noise, and the key concepts underlying sparsity-based image denoising algorithms Reviews the most successful variational approaches for image reconstruction, and introduces convolutional neural network-based denoising methods Provides an overview of the use of Gaussian priors for patch-based image denoising, and examines the potential of internal denoising Discusses selection and estimation strategies for patch-based video denoising, and explores how noise enters the imaging pipeline Surveys the properties of real camera noise, and outlines a fast approximation of nonlocal means filtering Proposes routes to improving denoising results via indirectly denoising a transform of the image, considering the right noise model and taking into account the perceived quality of the outputs This concise and clearly written volume will be of great value to researchers and professionals working in image processing and computer vision. The book will also serve as an accessible reference for advanced undergraduate and graduate students in computer science, applied mathematics, and related fields. Marcelo Bertalmío is a Professor in the Department of Information and Communication Technologies at Universitat Pompeu Fabra, Barcelona, Spain.
Cameras have become ubiquitous leading to an increase in the amount of video and image data captured by amateurs and professionals alike. Their ease of deployability makes them a great sensor for security applications as well. Hence, there is an ever-growing need to efficiently process and enhance captured image and videos for improving the performance of subsequent computer vision algorithms or simply for aesthetic reasons. To address this need, we focus on creating efficient techniques for large scale image and video denoising with varying degrees of genericity. We start by introducing a robust patch matching technique that increases the efficacy of denoising algorithms that build patch-specific filters. We show that using our matching criterion in multiple leading denoising algorithms provides additional performance gains over using default distance metrics. Next, we present a strategy to extend patch-based image denoising algorithms into a decompressed video denoising paradigm without increasing computational complexity. We leverage pre-calculated motion vectors present in a compressed video's bitstream to establish temporal correspondences, thus keeping the per-frame complexity of the video denoising algorithm equivalent to that of the corresponding image denoising method. Following this, we relax the patch-specific constraint on design of denoising filters leading to one of the fastest algorithms that uses targeted local patch prior. Recognizing that a targeted patch prior could be a limiting factor for a wide variety of natural images, we develop an efficient denoising algorithm that uses a Gaussian Mixture Model (GMM) to model a generic patch prior for image restoration. It is two orders of magnitude faster than similar methods while providing a very competitive quality-vs-speed operating curve. The final work presented in this thesis improves upon GMM priors by proposing a more expressive distribution using Generalized Gaussian Mixture Models (GGMM) patch priors. We circumvent the prohibitive computational complexity of using GGMM patch priors for image restoration by introducing asymptotically accurate but computationally efficient approximations to the bottlenecks encountered in this formulation. Our evaluations indicate that the GGMM prior is consistently a better fit for modeling image patch distribution and performs better on average in image denoising task.
In this paper, a denoising method based on dictionary learning has been proposed. With the increasing use of digital images, the methods that can remove noise based on image content and not restrictedly based on statistical properties has been widely extended. The major weakness of dictionary learning methods is that all of these methods require a long training process and a very large storage memory for storing features extracted from the training images. In the proposed method, using the concept of sparse matrix and similarities between samples extracted of similar images and adaptive filters the training process of dictionary based on ideal images have been simplified. Finally Images are checked based on its content by implicit optimization of memory usage and image noise will be removed with a minimum loss of stored samples in existing dictionary. At the end, the proposed method is implemented and results are shown its capabilities in comparison with other methods.
In general the digital image will be contaminated with noise. Retrieval of actual image by removing of noise is referred to as image denoising. The development of algorithms for image denoising is one of the most challenging task because removal of noise from the medical image should be done without destroying particular textures of image that are important for medical diagnosis and treatment. Recently, several denoising algorithms are proposed by many researchers in literature in order to preserve the important textures of medical images.
Advances in technological devices unveil new architectures for instrumentation and improvements in measurement techniques. Sensing technology, related to biomedical aspects, plays a key role in nowadays applications; it promotes different advantages for: healthcare, solving difficulties for elderly persons, clinical analysis, microbiological characterizations, etc.. This book intends to illustrate and to collect recent advances in biomedical measurements and sensing instrumentation, not as an encyclopedia but as clever support for scientists, students and researchers in other to stimulate exchange and discussions for further developments.
Image denoising is the process of recovering the original clean image from noise contaminations. It is an essential preprocessing step for many computer vision and image processing tasks such as segmentation, classification, detection, and tracking, since even a tiny bit of noise contaminations will greatly impact the performance of these operations. Over the past decades, numerous researches have been performed to investigate the nature of image noise and the approaches to remove it. From the simple spatial filters to the current popular deep neural networks, substantial progress has been made in the field of image denoising, but the search for new ways of improvements never ends. In this dissertation, we take the challenge of image denoising under multi-view scenarios. Suppose a number of images are captured by a camera array in which the cameras are positioned on a rectangular grid with equal distances. Apart from the intra-view correlation that is utilized in conventional single image denoising approaches, the image pixels across different views also have strong correlations with each other, thus providing abundant information about the original clean image data that can be used for recovering. This correlation across different views is also known as the inter-view correlation. To exploit the inter-view correlation from multiple images, we propose a patch-based multi-view denoising algorithm that employs the nonlocal self-similarity prior of natural images. In order to better capture the image correspondence between different views, and to avoid the exhaustive patch matching in the 3D searching space, we introduce a novel image data structure called 3D focus image stacks (3DFIS). The proposed algorithm first constructs a number of 3DFIS with respect to different disparity values that range from a preset minimum to maximum disparity. Disparity map for the target view is then estimated from the 3DFIS using robust texture-based view selection and patch-size variation. With the information from both 3DFIS and the disparity map, the target view is denoised from other views through a low-rank minimization approach that incorporates patch volume searching using a robust similarity metrics, as well as a novel occlusion handling technique. Through extensive experiments, we demonstrate that the proposed approach outperforms existing single image and multi-view denoising algorithms in terms of PSNR. In recent years, the rise of deep neural networks has significantly reshaped the field of computer vision and image processing. Due to its capability of capturing pixel correlations and parameter reduction, convolutional neural network (CNN) shows great potential in a broad category of image tasks, including image denoising. In response, based on the 3DFIS, we propose a convolutional neural network that is able to handle multiple views as the input and output. We call our network MVCNN. With the 3DFIS being constructed, the MVCNN is trained to process each 3DFIS and generate a denoised image stack that contains the recovered image information for regions of particular disparities. The denoised image stacks are then fused together to produce a denoised target view image using the estimated disparity map. Different from conventional multi-view denoising approaches that group similar patches first and then perform denoising on those patches, our CNN-based algorithm saves the effort of exhaustive patch searching and greatly reduces the computational time. As this dissertation will show, through our extensive investigation into the problem of multi-view image denoising using both the conventional patch-based nonlocal self-similarity prior and the revolutionary convolutional neural network, we have achieved the state-of-the-art performance that represents a new effort in dealing with multi-view image denoising.
The Image denoising naturally corrupted by noise is a classical problem in the field of signal or image processing. Denoising of a natural images corrupted by Gaussian noise using multi-wavelet techniques are very effective because of its ability to capture the energy of a signal in few energy transfer values. Multi-wavelet can satisfy with symmetry and asymmetry which are very important characteristics in signal processing. The better denoising result depends on the degree of the noise. Generally, its energy is distributed over low frequency band while both its noise and details are distributed over high frequency band. Corresponding hard threshold used in different scale high frequency sub-bands. This work is proposed to indicate the suitability of different wavelet and multi-wavelet based and a size of different neighborhood on the performance of image denoising algorithm in terms of PSNR value. Finally it compares wavelet and multi-wavelet techniques and produces best denoised image using multi-wavelet technique based on the performance of image denoising algorithm in terms of PSNR Values.
The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting. Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.
