Functional Neural Networks for Parametric Image Restoration Problems (original) (raw)
Related papers
Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression
2021
T HE huge success of deep-learning–based approaches in computer vision has inspired research in learned solutions to classic image/video processing problems, such as denoising, deblurring, dehazing, deraining, super-resolution (SR), and compression. Hence, learning-based methods have emerged as a promising nonlinear signal-processing framework for image/video restoration and compression. Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods. Hence, the state of the art in image restoration and compression is getting redefined. This special issue covers the state of the art in learned image/video restoration and compression to promote further progress in innovative architectures and training methods for effective and efficient networks for image/video restoration and compression. In the following, we provide a short overview of the state of the art in learned image and video ...
Image restoration using deep learning
2016
We propose a new image restoration method that reduces noise and blur in degraded images. In contrast to many state of the art methods, our method does not rely on intensive iterative approaches, instead it uses a pre-trained convolutional neural network.
Loss Functions for Image Restoration With Neural Networks
IEEE Transactions on Computational Imaging, 2017
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is 2. In this paper, we bring attention to alternative choices for image restoration. In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer. We compare the performance of several losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.
Video restoration based on deep learning: a comprehensive survey
Video restoration concerns the recovery of a clean video sequence starting from its degraded version. Different video restoration tasks exist, including denoising, deblurring, super-resolution, and reduction of compression artifacts. In this paper, we provide a comprehensive review of the main features of existing video restoration methods based on deep learning. We focus our attention on the main architectural components , strategies for motion handling, and loss functions. We analyze the standard benchmark datasets and use them to summarize the performance of video restoration methods, both in terms of effectiveness and efficiency. In conclusion, the main challenges and future research directions in video restoration using deep learning are highlighted.
Weight assignment for adaptive image restoration by neural networks
IEEE Transactions on Neural Networks, 2000
This paper presents a scheme for adaptively training the weights, in terms of varying the regularization parameter, in a neural network for the restoration of digital images. The flexibility of neural-network-based image restoration algorithms easily allow the variation of restoration parameters such as blur statistics and regularization value spatially and temporally within the image. This paper focuses on spatial variation of the regularization parameter. We first show that the previously proposed neural-network method based on gradient descent can only find suboptimal solutions, and then introduce a regional processing approach based on local statistics. A method is presented to vary the regularization parameter spatially. This method is applied to a number of images degraded by various levels of noise, and the results are examined. The method is also applied to an image degraded by spatially variant blur. In all cases, the proposed method provides visually satisfactory results in an efficient way.
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.
IJIRIS:: AM Publications,India, 2024
Image restoration is an integral component of computer vision that tries to restore pictures that have been deteriorated or corrupted to their original or enhanced condition. In this study, we look into the wide picture restoration techn models. There perform quite well, particularly when i rely on handcrafted filters restricts their adaptation to more complicated forms of been revolutionized by deep learning, which is led by co learning sophisticated representations of visual data. It is because of this that CNNs are able to deal with a wide variety of degradations, such as noise, blurring, artifacts, and missing data. Ge GANs, are continually pushing the limits of what is possible by utilizing adversarial training to accomplish spectacular outcomes in the areas of to overcome: Understanding limited interpretability of the the training of successful models may be quite computationally rigorous. make navigation revolutionize image processing and analysis, ultimately contributing to advancements across a wide range of scientific and technological domains. This can be concentrating on the promising research directions that are currently being pursued.
Can fully convolutional networks perform well for general image restoration problems?
2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), 2017
We present a fully convolutional network(FCN) based approach for color image restoration. FCNs have recently shown remarkable performance for high-level vision problem like semantic segmentation. In this paper, we investigate if FCN models can show promising performance for low-level problems like image restoration as well. We propose a fully convolutional model, that learns a direct end-to-end mapping between the corrupted images as input and the desired clean images as output. Our proposed method takes inspiration from domain transformation techniques but presents a data-driven task specific approach where filters for novel basis projection, task dependent coefficient alterations, and image reconstruction are represented as convolutional networks. Experimental results show that our FCN model outperforms traditional sparse coding based methods and demonstrates competitive performance compared to the state-of-the-art methods for image denoising. We further show that our proposed model can solve the difficult problem of blind image inpainting and can produce reconstructed images of impressive visual quality.
Real-Time Image Restoration with an Artificial Neural Network
. We present a neural network that can be applied to image correction in a preprocessing unit. Blur, geometric distortion and unequal brightness distribution are typical for many scanning techniques and can lead to difficulties during further processing of an image. These and other effects of image degradation which frequently appear spacevariant can be considered simultaneously by this approach. In order to calibrate the correcting system the weights of a neural network are trained. Using suitable training patterns and an appropriate optimization criterion for the degraded images, in the result the dimensioned network represents a space-variant filter with a behavior similar to the well-known Wiener filter. The restoration result can be easily altered by the scheme of the learning data generation. Theoretical considerations and examples for 1-D, 2-D and 3-D implementations in soft- and hardware are given. 1. Introduction When operating with image signals, irregularities of the scan...