Learning Joint Demosaicing and Denoising Based on Sequential Energy Minimization

Teresa Klatzer, Kerstin Hammernik, Patrick Knöbelreiter, Thomas Pock

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Demosaicing is an important first step for color image acquisition. For practical reasons, demosaicing algorithms have to be both efficient and yield high quality results in the presence of noise. The demosaicing problem poses several challenges, e.g. zippering and false color artifacts as well as edge blur. In this work, we introduce a novel learning based method that can overcome these challenges. We formulate demosaicing as an image restoration problem and propose to learn efficient regularization inspired by a variational energy minimization framework that can be trained for different sensor layouts. Our algorithm performs joint demosaicing and denoising in close relation to the real physical mosaicing process on a camera sensor. This is achieved by learning a sequence of energy minimization problems composed of a set of RGB filters and corresponding activation functions. We evaluate our algorithm on the Microsoft Demosaicing data set in terms of peak signal to noise ratio (PSNR) and structured similarity index (SSIM). Our algorithm is highly efficient both in image quality and run time. We achieve an improvement of up to 2.6 dB over recent state-of-the-art algorithms.
Original languageEnglish
Title of host publicationIEEE International Conference on Computational Photography (ICCP)
Publication statusPublished - 13 May 2016
EventInternational Conference of Computational Photography - Evanston, IL, United States
Duration: 13 May 201615 May 2016


ConferenceInternational Conference of Computational Photography
Country/TerritoryUnited States
CityEvanston, IL


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