Abstract
Images are susceptible to various kinds of noises, which corrupt the pictorial information stored in the images. Image de-noising has become an integral part of the image processing workflow. It is used to attenuate the noises and accentuate the specific image information stored within. Machine learning is an important tool in the image-de-noising workflow in terms of its robustness, accuracy, and time requirement. This paper explores the numerous state-of-the-art machine-learning-based image de-noisers like dictionary learning models, convolutional neural networks and generative adversarial networks for a range of noises like Gaussian, Impulse, Poisson, Mixed and Real-World noises. The motivation, algorithm and framework of different machine learning de-noisers are analyzed. These de-noisers are compared using PSNR as quality assessment metric on some benchmark datasets. The best de-noising results for different noise types are discussed along with future prospects. Among various Gaussian noise de-noisers, GCBD, BRDNet and PReLU network prove to be promising. CNN+LSTM, and MC2RNet are most suitable CNN-based Poisson de-noisers. For impulse noise removal, Blind CNN, and CNN+PSO perform well. For mixed noise removal, WDL, EM-CNN, CNN, SDL, and Mixed CNN are prominent. De-noisers like GRDN and DDFN show accurate results in the domain of real-world de-noising.
Highlights
Image de-noising has played a pivotal role in recent years with the advent of many latest computer vision applications
RESULT AND DISCUSSION Out of all machine learning methods, dictionary learning models performance is inferior in terms of peak signal to noise ratio (PSNR)
The machine learning models have evolved from fully connected neural networks to convolutional neural network (CNN) based de-noisers
Summary
Image de-noising has played a pivotal role in recent years with the advent of many latest computer vision applications. B. CLASSIFICATION OF IMAGE DE-NOISING TECHNIQUES The image de-noising methods can be grouped into spatial domain techniques, transform domain techniques, fuzzy filtering-based techniques, and machine learning techniques [13], [14]. C. MACHINE LEARNING BASED IMAGE DE-NOISING The machine learning image de-noising techniques have made considerable progress with introducing benchmark datasets for a particular application, deep learning advancements, and increased computational power with Graphical Processing Unit's (GPU's). MACHINE LEARNING BASED IMAGE DE-NOISING The machine learning image de-noising techniques have made considerable progress with introducing benchmark datasets for a particular application, deep learning advancements, and increased computational power with Graphical Processing Unit's (GPU's) They are further broadly classified into sparsity-based dictionary learning models, multi-layer perceptron models, convolutional neural network-based models, and generative adversarial network-based models.
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