Flashlight CNN Image Denoising (original) (raw)

Deep Convolutional Denoising of Low-Light Images

ArXiv, 2017

Poisson distribution is used for modeling noise in photon-limited imaging. While canonical examples include relatively exotic types of sensing like spectral imaging or astronomy, the problem is relevant to regular photography now more than ever due to the booming market for mobile cameras. Restricted form factor limits the amount of absorbed light, thus computational post-processing is called for. In this paper, we make use of the powerful framework of deep convolutional neural networks for Poisson denoising. We demonstrate how by training the same network with images having a specific peak value, our denoiser outperforms previous state-of-the-art by a large margin both visually and quantitatively. Being flexible and data-driven, our solution resolves the heavy ad hoc engineering used in previous methods and is an order of magnitude faster. We further show that by adding a reasonable prior on the class of the image being processed, another significant boost in performance is achieved.

State‐of‐art analysis of image denoising methods using convolutional neural networks

IET Image Processing, 2019

Convolutional neural networks (CNNs) are deep neural networks that can be trained on large databases and show outstanding performance on object classification, segmentation, image denoising etc. In the past few years, several image denoising techniques have been developed to improve the quality of an image. The CNN based image denoising models have shown improvement in denoising performance as compared to non-CNN methods like block-matching and three-dimensional (3D) filtering, contemporary wavelet and Markov random field approaches etc. which had remained state-of-the-art for years. This study provides a comprehensive study of state-of-the-art image denoising methods using CNN. The literature associated with different CNNs used for image restoration like residual learning based models (DnCNN-S, DnCNN-B, IDCNN), non-locality reinforced (NN3D), fast and flexible network (FFDNet), deep shrinkage CNN (SCNN), a model for mixed noise reduction, denoising prior driven network (PDNN) are reviewed. DnCNN-S and PDNN remove Gaussian noise of fixed level, whereas DnCNN-B, IDCNN, NN3D and SCNN are used for blind Gaussian denoising. FFDNet is used for spatially variant Gaussian noise. The performance of these CNN models is analysed on BSD-68 and Set-12 datasets. PDNN shows the best result in terms of PSNR for both BSD-68 and Set-12 datasets.

Dilated Residual Network for Image Denoising

arXiv preprint arXiv: 1708.05473, 2017

Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of noisy and clean image patches. Most existing CNN models for image denoising have many layers. In such cases, the models involve a large amount of parameters and are computationally expensive to train. In this paper, we develop a dilated residual CNN for Gaussian image denoising. Compared with the recently proposed residual denoiser, our method can achieve comparable performance with less computational cost. Specifically, we enlarge receptive field by adopting dilated convolution in residual network, and the dilation factor is set to a certain value. Appropriate zero padding is utilized to make the dimension of the output the same as the input. It has been proven that the expansion of receptive field can boost the CNN performance in image classification, and we further demonstrate that it can also lead to competitive performance for denoising problem. Moreover, we present a formula to calculate receptive field size when dilated convolution is incorporated. Thus, the change of receptive field can be interpreted mathematically. To validate the efficacy of our approach, we conduct extensive experiments for both gray and color image denoising with specific or randomized noise levels. Both of the quantitative measurements and the visual results of denoising are promising comparing with state-of-the-art baselines.

ResDNN: deep residual learning for natural image denoising

IET Image Processing, 2020

Image denoising is a thoroughly studied research problem in the areas of image processing and computer vision. In this work, a deep convolution neural network with added benefits of residual learning for image denoising is proposed. The network is composed of convolution layers and ResNet blocks along with rectified linear unit activation functions. The network is capable of learning end-to-end mappings from noise distorted images to restored cleaner versions. The deeper networks tend to be challenging to train and often are posed with the problem of vanishing gradients. The residual learning and orthogonal kernel initialisation keep the gradients in check. The skip connections in the ResNet blocks pass on the learned abstractions further down the network in the forward pass, thus achieving better results. With a single model, one can tackle different levels of Gaussian noise efficiently. The experiments conducted on the benchmark datasets prove that the proposed model obtains a significant improvement in structural similarity index than the previously existing state-of-the-art techniques.

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 ...

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.

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...

DN-ResNet: Efficient Deep Residual Network for Image Denoising

Computer Vision – ACCV 2018

A deep learning approach to blind denoising of images without complete knowledge of the noise statistics is considered. We propose DN-ResNet, which is a deep convolutional neural network (CNN) consisting of several residual blocks (ResBlocks). With cascade training, DN-ResNet is more accurate and more computationally efficient than the state of art denoising networks. An edge-aware loss function is further utilized in training DN-ResNet, so that the denoising results have better perceptive quality compared to conventional loss function. Next, we introduce the depthwise separable DN-ResNet (DS-DN-ResNet) utilizing the proposed Depthwise Seperable ResBlock (DS-ResBlock) instead of standard ResBlock, which has much less computational cost. DS-DN-ResNet is incrementally evolved by replacing the ResBlocks in DN-ResNet by DS-ResBlocks stage by stage. As a result, high accuracy and good computational efficiency are achieved concurrently. Whereas previous state of art deep learning methods focused on denoising either Gaussian or Poisson corrupted images, we consider denoising images having the more practical Poisson with additive Gaussian noise as well. The results show that DN-ResNets are more efficient, robust, and perform better denoising than current state of art deep learning methods, as well as the popular variants of the BM3D algorithm, in cases of blind and non-blind denoising of images corrupted with Poisson, Gaussian or Poisson-Gaussian noise. Our network also works well for other image enhancement task such as compressed image restoration.

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...

Dense-Sparse Deep CNN Training for Image Denoising

ArXiv, 2021

Recently, deep learning (DL) methods such as the convolutional neural networks (CNNs) have gained prominence in the area of image denoising. This is owing to their proven ability to surpass state-of-the-art classical image denoising algorithms such as BM3D. Deep denoising CNNs (DnCNNs) use many feedforward convolution layers with added regularization methods of batch normalization and residual learning to improve denoising performance significantly. However, this comes at the expense of a huge number of trainable parameters. In this paper, we address this issue by reducing the number of parameters while achieving comparable level of performance. We derive motivation from the improved performance obtained by training networks using the dense-sparse-dense (DSD) training approach. We extend this training approach to a reduced DnCNN (RDnCNN) network resulting in a faster denoising network with significantly reduced parameters and comparable performance to the DnCNN.