A Deep Boltzmann Machine-Based Approach for Robust Image Denoising (original) (raw)
Related papers
A Robust Restricted Boltzmann Machine for Binary Image Denoising
2017
During the image acquisition process, some level of noise is usually added to the real data mainly due to physical limitations of the acquisition sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be processed in order to attenuate its noise without loosing details. Machine learning approaches have been successfully used for image denoising. Among such approaches, Restricted Boltzmann Machine (RBM) is one of the most used technique for this purpose. Here, we propose to enhance the RBM performance on image denoising by adding a posterior supervision before its final denoising step. To this purpose, we propose a simple but effective approach that performs a fine-tuning in the RBM model. Experiments on public datasets corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach with respect to some state-of-the-art image denoising approaches.
Image Denoising in the Deep Learning Era
Over the last decade, the number of digital images captured per day witnessed a massive explosion. Nevertheless, the visual quality of photographs is often degraded by noise during image acquisition or transmission. With the re-emergence of deep neural networks, the performance of image denoising techniques has been substantially improved in recent years. The objective of this paper is to provide a comprehensive survey of recent advances in image denoising techniques based on deep neural networks. In doing so, we commence with a thorough description of the fundamental preliminaries of the image denoising problem followed by highlighting the benchmark datasets and the widely used metrics for objective assessments. Subsequently, we study the existing deep denoisers in the supervised and unsupervised categories and review the technical specifics of some representative methods within each category. Last but not least, we conclude the analysis by remarking on trends and challenges in the...
State of the Art on: Deep Image Denoising
2020
Recent technological and methodological advances have allowed the employment of deep learning techniques, in particular deep artificial neural networks, in a large variety of fields. One of the fields that most is benefiting from the introduction of deep learning is image processing and computer vision, which mainly exploits convolutional neural networks (CNNs) for addressing visual understanding problems. For instance, the use of CNNs for image classification and object detection has led to outstanding results. In the last years, deep learning models have been successfully employed also for the tasks of image restoration. Starting from a corrupted image (e.g. noisy, blurred), the goal of image restoration is to recover the original image (i.e. the clean image). Depending on the type of corruption, image restoration tasks can be divided into deblurring, super-resolution, denoising, inpainting, text removal and many others. In particular, the main focus of our research work is image ...
Image noise reduction by deep learning methods
International Journal of Electrical and Computer Engineering (IJECE), 2023
Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.
From Classical to Deep Learning: A Systematic Review of Image Denoising Techniques
JURNAL ILMIAH COMPUTER SCIENCE (JICS), 2024
Image denoising is essential in image processing and computer vision, aimed at removing noise while preserving critical features. This review compares classical methods like Gaussian filtering and wavelet transforms with modern deep learning techniques such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). We conducted a systematic literature review from [start year] to [end year], analyzing studies from IEEE Xplore, PubMed, and Google Scholar. Classical methods are effective for simple noise models but struggle with fine detail preservation. In contrast, deep learning excels in both noise reduction and detail retention, supported by metrics like PSNR and SSIM. Hybrid approaches combining classical and deep learning show promise for balancing performance and computational efficiency. Overall, deep learning leads in handling complex noise patterns and preserving high-detail images. Future research should focus on optimizing deep learning models, exploring unsupervised learning, and extending denoising applications to real-time and large-scale image processing.
Research Project Proposal: Deep Image Denoising
2020
The goal of image restoration is to recover the original, clean image, starting from a corrupted image. Depending on the type of corruption, image restoration tasks can be divided into deblurring, super-resolution, denoising, inpainting, text removal and many others. In particular, image denoising has the goal of estimating the clean image from its observed version corrupted by noise. Modern image denoising lies at the intersection of signal processing, computer science and machine learning. Indeed, recent technological and methodological advances in deep learning have allowed the employment of convolutional neural networks (CNNs) for image restoration purposes. The obvious application for image denoising is to provide to the user a pleasant and clear image by removing as much noise as possible, without losing details. From a technical point of view, a denoised image is an essential prerequisite for more high-level computer vision tasks and complex deep learning pipelines (e.g. auto...
2009
We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent expectations are estimated using a variational approximation that tends to focus on a single mode, and dataindependent expectations are approximated using persistent Markov chains. The use of two quite different techniques for estimating the two types of expectation that enter into the gradient of the log-likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer "pre-training" phase that allows variational inference to be initialized with a single bottomup pass. We present results on the MNIST and NORB datasets showing that deep Boltzmann machines learn good generative models and perform well on handwritten digit and visual object recognition tasks.
An Analysis of Gaussian-Binary Restricted Boltzmann Machines for Natural Images
A Gaussian-binary restricted Boltzmann machine is a widely used energy-based model for continuous data distributions, although many authors reported difficulties in training on natural images. To clarify the model's capabilities and limitations we derive a rewritten formula of the probability density function as a linear superposition of Gaussians. Based on this formula we show how Gaussian-binary RBMs learn natural image statistics. However the probability density function of the model is not a good representation of the data distribution.
Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image Statistics
We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models. The key aspect of this analysis is to show that GRBMs can be formulated as a constrained mixture of Gaussians, which gives a much better insight into the model's capabilities and limitations. We show that GRBMs are capable of learning meaningful features both in a two-dimensional blind source separation task and in modeling natural images. Further, we show that reported difficulties in training GRBMs are due to the failure of the training algorithm rather than the model itself. Based on our analysis we are able to propose several training recipes, which allowed successful and fast training in our experiments. Finally, we discuss the relationship of GRBMs to several modifications that have been proposed to improve the model.
Suppression of Independent and Correlated Noise with Similarity-based Unsupervised Deep Learning
2020
I denoising is to recover signals hidden under a noisy appearance with numerous applications in many fields. Since noise is a statistical fluctuation governed by quantum mechanics, denoising is generally achieved by a mean/averaging operation. For example, local averaging methods can perform Gaussian smoothing (1), anisotropic filtering(2, 3), neighborhood filtering (4–6), and transform domain processing (7). On the other hand, nonlocal averaging methods use various nonlocal means with Gaussian kernel based weighting (8), or via nonlocal collaborative filtering in a transform domain (9, 10). Remarkably, the nonlocal methods usually outperform the local methods, as images usually consist of repeated patterns that can be leveraged in recovering signals coherently. Over the past several years, the area of image denoising has been dominated by deep convolutional neural networks (CNNs) (11). Different from the traditional methods that directly denoise an image based on an explicit model,...