Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty

Image demosaicking and denoising are the two key fundamental steps in digital camera pipelines, aiming to reconstruct clean color images from noisy luminance readings. In this paper, we propose and study Wild-JDD, a novel learning framework for joint demosaicking and denoising in the wild. In contrast to previous works which generally assume the ground truth of training data is a perfect reflection of the reality, we consider here the more common imperfect case of ground truth uncertainty in the wild. We first illustrate its manifestation as various kinds of artifacts including zipper effect, color moire and residual noise. Then we formulate a two-stage data degradation process to capture such ground truth uncertainty, where a conjugate prior distribution is imposed upon a base distribution. After that, we derive an evidence lower bound (ELBO) loss to train a neural network that approximates the parameters of the conjugate prior distribution conditioned on the degraded input. Finally, to further enhance the performance for out-of-distribution input, we design a simple but effective fine-tuning strategy by taking the input as a weakly informative prior. Taking into account ground truth uncertainty, Wild-JDD enjoys good interpretability during optimization. Extensive experiments validate that it outperforms state-of-the-art schemes on joint demosaicking and denoising tasks on both synthetic and realistic raw datasets.Comment: Accepted by AAAI202

Improved Thermal and Electromagnetic Shielding of PEEK Composites by Hydroxylating PEK-C Grafted MWCNTs

With the rapid rise of new technologies such as 5G and artificial intelligence, electronic products are becoming smaller and higher power, and there is an increasing demand for electromagnetic interference shielding and thermal conductivity of electronic devices. In this work, hydroxyphenolphthalein type polyetherketone grafted carboxy carbon nanotube (PEK-C-OH-g-MWCNTs-COOH) composites were prepared by esterification reaction. The composites exhibited good thermal conductivity, and compared with (MWCNTs-COOH/PEEK) with randomly distributed fillers, (PEK-C-OH-g-MWCNTs-COOH) composites showed a significant advantage, with the same carbon nanotube content, the thermal conductivity of PEK-C-OH-g-MWCNTs-COOH/PEEK (30 wt%) was 0. 71 W/(m-K), which was 206% higher than that of PEEK and 0.52 W/(m-K) higher than that of MWCNTs-COOH/PEEK (26.1 wt%). In addition, the PEK-C-OH-g-MWCNTs-COOH) composite exhibited excellent electrical conductivity and electromagnetic shielding (SE). The SE of 30 wt% PEK-C-OH-g-MWCNTs-COOH/PEEK is higher than the commercially used standard whose value is 22.9 dB (8.2 GHz). Thus, this work provides ideas for the development of thermally conductive functionalized composites