Fast External Denoising Using Pre-Learned Transformations

• Published in: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp. 1025 - 1033
• Main Authors: Parameswaran, Shibin, Enming Luo, Deledalle, Charles-Alban, Nguyen, Truong Q.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2017
• Subjects: Algorithm design and analysis; Image denoising; Noise measurement; Noise reduction; Runtime; Videos
• ISSN: 2160-7516
• DOI: 10.1109/CVPRW.2017.139

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.