Abstract
This paper proposes a learning-based denoising method called FlashLight CNN (FLCNN) that implements a deep neural network for image denoising. The proposed approach is based on deep residual networks and inception networks and it is able to leverage many more parameters than residual networks alone for denoising grayscale images corrupted by additive white Gaussian noise (AWGN). FlashLight CNN demonstrates state of the art performance when compared quantitatively and visually with the current state of the art image denoising methods.
| Original language | English |
|---|---|
| Title of host publication | 28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings |
| Publisher | IEEE |
| Pages | 670-674 |
| Number of pages | 5 |
| ISBN (Electronic) | 9789082797053 |
| DOIs | |
| Publication status | Published - 2020 |
| Publication type | A4 Article in conference proceedings |
| Event | European Signal Processing Conference - Amsterdam, Netherlands Duration: 24 Aug 2020 → 28 Aug 2020 |
Publication series
| Name | European Signal Processing Conference |
|---|---|
| Volume | 2021-January |
| ISSN (Print) | 2219-5491 |
Conference
| Conference | European Signal Processing Conference |
|---|---|
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 24/08/20 → 28/08/20 |
Funding
This work is in part supported by the Business Finland (project 3418 - E!7632 ITEA3 COMPACT, 2017-2020)
Keywords
- Convolutional Neural Networks
- Gaussian Noise
- Image Denoising
- Inception
- Residual Learning
Publication forum classification
- Publication forum level 1
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering