Implementation of Image Denoising Using Deep Neural Network
Dr.S.Suresh Kumar, V. S. Nishok, Mr.V.Jaikumar, Prasad Jones Christydass · Zenodo (CERN European Organization for Nuclear Research) · 2020
Numerous researchers have looked into the potential of deep learning methods for use in image denoising. But there are significant distinctions between the various deep learning approaches to image denoising. Discriminative learning based on deep learning is especially useful for combating the effects of Gaussian noise. Effective estimation of real-world noise is possible with deep learning-based optimization algorithms. This diversity of approaches has led to a lack of research comparing deep learning methods for image denoising. And which of the following is In this research, we evaluate various approaches to deep picture denoising and provide a comparison. First, we divide images into four groups based on the type of convolutional neural network (CNN) they were processed through: CNNs trained on incremental white noise, CNNs trained on true noise, CNNs trained on the blind wavelet transform, and CNNs trained on composite noisy images that combine low-resolution, noisy, and blurry images. Then, we take a look at the goals and underlying ideas shared by the several deep learning approaches. Then, we utilise quantitative and qualitative evaluations on publically available denoised datasets to compare state-of-the-art approaches. We wrap up by outlining a handful of issues and future research directions.