Image noise reduction by deep learning methods (original) (raw)

Noise Estimation and Type Identification in Natural Scene and Medical Images using Deep Learning Approaches

Contrast Media & Molecular Imaging

The image enhancement for the natural images is the vast field where the quality of the images degrades based on the capturing and processing methods employed by the capturing devices. Based on noise type and estimation of noise, filter need to be adopted for enhancing the quality of the image. In the same manner, the medical field also needs some filtering mechanism to reduce the noise and detection of the disease based on the clarity of the image captured; in accordance with it, the preprocessing steps play a vital role to reduce the burden on the radiologist to make the decision on presence of disease. Based on the estimated noise and its type, the filters are selected to delete the unwanted signals from the image. Hence, identifying noise types and denoising play an important role in image analysis. The proposed framework addresses the noise estimation and filtering process to obtain the enhanced images. This paper estimates and detects the noise types, namely Gaussian, motion a...

Noise Reduction in Images Using Autoencoders

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Ideally, the signals which are pure can exist only on paper. As there are some techniques for denoising the provided signal up to some degree, so procedure during that time it is important that such techniques must be reconcilable with the most of the devices. This article describes a for denoising with the help of an autoencoder using image processing technique and algorithms which are based on deep learning. With the aid of autoencoders, noise reduction is not accomplished using a conventional method in which the output signal is essentially the same signal that was used as an input previously. Here the main focus remains originality as the autoencoder follows a back propagation process It is one of the approaches that focuses on the techniques described in this article are interchangeable. i.e., Working for any signal and having, reliability, efficient Ness and compatibility with more devices.

Medical Image Denoising using Deep Learning

2019

In medical field, image denoising is must for analysis of images, diagnosis and treatment of diseases. Now a day, image denoising methods based on deep learning are effective. We determine the quality of the denoised image, peak signal to noise ratio (PSNR), Mean Square Error(MSE) and compare with training data set. An experimental result shows that our approach has better performance than some other techniques. In this paper, we present a new image denoising techniques for removing salt and paper noise from corrupted image by using deep learning.

Image Restoration and Enhancement Using Deep Learning

International Journal of Engineering Applied Sciences and Technology

During the process of image acquisition, sometimes images are degraded because of various reasons like low resolution of camera, motion blur, noise etc. This paper presents the work associated with the Image Restoration & Enhancement techniques. The process of recovering degraded image is known as Image Restoration. Image restoration includes denoising image, image inpainting, etc. Here we proposed Convolution Neural Network (CNN) with Median Filter for removing noise, Region filling Exemplar Based Inpainting Algorithm for image inpainting. Image enhancement is one amongst the problem in image processing. Haze, low lighting etc. are the various problems in images. The aim of Image enhancement is to process an image such that result is more suitable than original image for specific application. Here for haze removal we implement dark channel prior algorithm and for lightning low-light image we proposed functions. Image enhancement improves the appearance of the image.

Noisy image enhancements using deep learning techniques

International Journal of Electrical and Computer Engineering (IJECE), 2024

This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object recognition in noisy images. Compared to single-tasking models, the multi-tasking model showed the superiority of this approach in performing multiple tasks simultaneously and saving training time. This study confirms the effectiveness of using multitasking models using deep learning for object recognition in noisy images. The results obtained can be applied in various fields, including computer vision, robotics, automatic driving, and others, where accurate object recognition in noisy images is a critical component.

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Sustainability

Recent innovations in digital image capturing techniques facilitate the capture of stationary and moving objects. The images can be easily captured via high-end digital cameras, mobile phones and other handheld devices. Most of the time, captured images vary compared to actual objects. The captured images may be contaminated by dark, grey shades and undesirable black spots. There are various reasons for contamination, such as atmospheric conditions, limitations of capturing device and human errors. There are various mechanisms to process the image, which can clean up contaminated image to match with the original one. The image processing applications primarily require detection of accurate noise type which is used as input for image restoration. There are filtering techniques, fractional differential gradient and machine learning techniques to detect and identify the type of noise. These methods primarily rely on image content and spatial domain information of a given image. With th...

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...

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.

Sound Noise Reduction Based on Deep Neural Networks

International Journal of Scientific Research in Science, Engineering and Technology, 2023

Audio transmittance is a generation that is now rapidly growing as a connectivity option for everyone around the world, demanding to experience the frictionless transfer of audio messages. Audio transmittance has a wide range of capabilities compared to other connectivity technologies. But we are living in the noisy world, hence while transmitting audio signal; we don’t only transmit audio, different types of noise gets transmitted with our audio signal as well which will lead to an unclear communication The basic purpose of this model is specifically focused on detecting and restoring noisy audio signals which consists various background noise. The removal of noise from the audio signal will enhance the information carrying capacity of the signal during audio communication. For the removal of noise from audio signal, a stacked Long Short Term Memory (LSTM) model is proposed. ‘Edinburgh DataShare’ dataset has been used to train the model. During the evaluation of model, the Huber loss of 0.0205 has been evaluated in 50 epochs which shows that the LSTM network was successfully implemented for noise removal of audio signal. Hence on the basis of result, we can conclude that that Stacked LSTM network works well in noise removal of audio signals

Deep Learning Based Filtering Algorithm for Noise Removal in Underwater Images

Water

Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, low contrast, and loss of detail (especially edge information). The paper proposes a method to address these issues by de-noising and increasing the resolution of the image using a model network trained on similar data. The network extracts frames from a video and filters them with a trigonometric–Gaussian filter to eliminate the noise in the image. It then applies contrast limited adaptive histogram equalization (CLAHE) to improvise the image contrast, and finally enhances the image resolution. Experimental results show that the proposed method could effectively produce enhanced images from degraded underwater images.