Fast External Denoising Using Pre-Learned Transformations

Shibin Parameswaran, Enming Luo, Charles-Alban Deledalle, Truong Q. Nguyen; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 29-37


We introduce a new external denoising algorithm that utilizes pre-learned transformations to accelerate filter calculations during runtime. The proposed fast external denoising (FED) algorithm shares characteristics of the powerful Targeted Image Denoising (TID) and Expected Patch Log-Likelihood (EPLL) algorithms. By moving computationally demanding steps to an offline learning stage, the proposed approach aims to find a balance between processing speed and obtaining high quality denoising estimates. We evaluate FED on three datasets with targeted databases (text, face and license plates) and also on a set of generic images without a targeted database. We show that, like TID, the proposed approach is extremely effective when the transformations are learned using a targeted database. We also demonstrate that FED converges to competitive solutions faster than EPLL and is orders of magnitude faster than TID while providing comparable denoising performance.

Related Material

author = {Parameswaran, Shibin and Luo, Enming and Deledalle, Charles-Alban and Nguyen, Truong Q.},
title = {Fast External Denoising Using Pre-Learned Transformations},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}