Proceedings Article10.1109/PDGC50313.2020.9315749
Correlative Analysis of Denoising Methods in Spectral Images Embedded with Different Noises
Sangeetha Annam,Anshu Singla +1 more
- 06 Nov 2020
3
TL;DR: In this paper, a correlative analysis of different noise removal methods using various filters in spectral images is performed, and the performances of the methods are evaluated using benchmarks: SignaltoNoise Ratio (SNR) and Peak Signal-to-N noise ratio (PSNR).
read more
Abstract: Digital image is one of the primary way of communication in the present digital world. During the acquiring process, the images may become noisy. Noise reduction is a demanding task during the image analysis process without dissimilating the important features. It is the procedure of restoring the original image by discarding unwanted noises and known as Image denoising. The main intention of any noise removal technique is to completely eradicate the noise from the image, such that the resulting image is better than the original image. In this digital era, remote sensing images are widely commercial for environmental monitoring. In this study, a correlative analysis of different noise removal methods using various filters in spectral images is performed. Spectral images are introduced with different types of noise and further filters are applied to denoise the image. The performances of the methods are evaluated using benchmarks: Signal-to-Noise Ratio (SNR) and Peak Signal-to-N oise Ratio (PSNR). Experimental results demonstrate that the SNR and PSNR measures were comparatively higher for all the filters when the image is introduced with Poisson noise.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image
TL;DR: In this paper , the authors used various kinds of filtering operators against three various noises, which are the signal-to-noise ratio comparison against the phantom image in spatial and frequency domain.
Comparison of denoising methods for hyperspectral images: DnCNN, NGM, CSF, BM3D and Wiener
Mehmet Akif Günen,Erkan Beşdok +1 more
TL;DR: In this paper , Pavia university's hyperspectral dataset with Gaussian, salt & pepper, poisson, and speckle noise were denoised using DnCNN, NGM, CSF, BM3D, and Wiener.
1
Enhancing Digital Image Forensics with Error Level Analysis (ELA)
Robert Idlbek,Mirko Pešić,Krešimir Šolić +2 more
- 20 May 2024
References
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
TL;DR: Zhang et al. as mentioned in this paper proposed a feed-forward denoising convolutional neural networks (DnCNNs) to handle Gaussian denobling with unknown noise level.
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
TL;DR: Zhang et al. as discussed by the authors proposed a denoising convolutional neural network (DnCNN) to handle Gaussian denoizing with unknown noise level, which implicitly removes the latent clean image in the hidden layers.
3.2K
Outlier detection for high dimensional data
Charu C. Aggarwal,Philip S. Yu +1 more
- 01 May 2001
TL;DR: New techniques for outlier detection which find the outliers by studying the behavior of projections from the data set are discussed.
1.2K
Brief review of image denoising techniques
TL;DR: This paper gives the formulation of the image denoising problem, and then it presents several imageDenoising techniques, which discuss the characteristics of these techniques and provide several promising directions for future research.
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network.
TL;DR: Wang et al. as discussed by the authors proposed a novel deep learning-based method by learning a non-linear end-to-end mapping between the noisy and clean HSIs with a combined spatial-spectral deep convolutional neural network (HSID-CNN).
449