• Corpus ID: 226278148

A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods

  title={A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods},
  author={Zhaoming Kong and Xiaowei Yang and Lifang He},
Filtering multi-dimensional images such as color images, color videos, multispectral images and magnetic resonance images is challenging in terms of both effectiveness and efficiency. Leveraging the nonlocal self-similarity (NLSS) characteristic of images and sparse representation in the transform domain, the block-matching and 3D filtering (BM3D) based methods show powerful denoising performance. Recently, numerous new approaches with different regularization terms, transforms and advanced… 

MMPDNet: Multi-Stage & Multi-Attention Progressive Image Denoising

A novel multi-stage and multi-attention architecture of CNN for image denoising, named MMPDNet, and its extensive experiment results deliver strong performance gains on some primary real-word denoised datasets, including SIDD and DND.

A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration

This paper advocates a hybrid approach based on sparse coding principles that retains the interpretability of classical techniques encoding domain knowledge with handcrafted image priors, while allowing to train model parameters end-to-end without massive amounts of data.

Plug-and-play optimization for pixel super-resolution phase re- trieval

In order to increase signal-to-noise ratio in measurement, most imaging detectors sacrifice resolution to increase pixel size in confined area. Although the pixel super-resolution technique (PSR)

Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones

This work presents a novel recurrent network consisting of with constant latency and memory consumption, called FloRNN, which aims to address the offline issue of BiRNN.



Color Image and Multispectral Image Denoising Using Block Diagonal Representation

This paper investigates the influence and potential of representation at patch level by considering a general formulation with a block diagonal matrix and shows that by training a proper global patch basis, along with a local principal component analysis transform in the grouping dimension, a simple transform-threshold-inverse method could produce very competitive results.

Non-local Color Image Denoising with Convolutional Neural Networks

  • Stamatios Lefkimmiatis
  • Computer Science
    2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
This work proposes a novel deep network architecture for grayscale and color image denoising that is based on a non-local image model and highlights a direct link of the proposed non- local models to convolutional neural networks.

Real-World Image Denoising with Deep Boosting

A lightweight Dense Dilated Fusion Network (DDFN) is designed as an embodiment of the boosting unit, which addresses the vanishing of gradients during training due to the cascading of networks while promoting the efficiency of limited parameters.

Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising

This paper proposes a multi-channel (MC) optimization model for real color image denoising under the weighted nuclear norm minimization (WNNM) framework, concatenate the RGB patches to make use of the channel redundancy, and introduces a weight matrix to balance the data fidelity of the three channels in consideration of their different noise statistics.

NLH: A Blind Pixel-Level Non-Local Method for Real-World Image Denoising

This paper introduces a pixel-level NSS prior, and proposes an accurate noise level estimation method, and develops a blind image denoising method based on the lifting Haar transform and Wiener filtering techniques.

Multichannel Nonlocal Means Fusion for Color Image Denoising

An advanced color image denoising scheme called multichannel nonlocal means fusion (MNLF), where noise reduction is formulated as the minimization of a penalty function, provides competitive performance both in terms of the color peak signal-to-noise ratio and in perceptual quality when compared with other state-of-the-art benchmarks.

Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization

  • Qi XieQian Zhao Lei Zhang
  • Computer Science
    2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
A new tensor-based denoising approach by fully considering two intrinsic characteristics underlying an MSI, i.e., the global correlation along spectrum (GCS) and nonlocal self-similarity across space (NSS) is proposed.

Universal Denoising Networks : A Novel CNN Architecture for Image Denoising

A novel network architecture for learning discriminative image models that are employed to efficiently tackle the problem of grayscale and color image denoising is designed and two different variants are introduced, which achieve excellent results under additive white Gaussian noise.

Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising

This paper proposes an effective MSI denoising approach by combinatorially considering two intrinsic characteristics underlying an MSI: the nonlocal similarity over space and the global correlation across spectrum.

A Brief Review of Real-World Color Image Denoising

This chapter presents a brief review of related methods and publicly available datasets, moreover, a new dataset that includes more natural outdoor scenes is introduced and discussion on visual effect enhancement is included.