This paper reviews the first NTIRE challenge on video super-resolution (restoration of rich details in lowresolution video frames) with focus on proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed standard bicubic downscaling setup while Track 2 had realistic dynamic motion blurs. Each competition had 124 and 104 registered participants. There were total 14 teams in the final testing phase. They gauge the state-of-the-art in video super-resolution.
Generation of synthetic data is a challenging task. There are only a few significant works on RGB video generation and no pertinent works on RGB-D data generation. In the present work, we focus our attention on synthesizing RGB-D data which can further be used as dataset for various applications like object tracking, gesture recognition, and action recognition. This paper has put forward a proposal for a novel architecture that uses conditional deep 3D-convolutional generative adversarial networks to synthesize RGB-D data by exploiting 3D spatio-temporal convolutional framework. The proposed architecture can be used to generate virtually unlimited data. In this work, we have presented the architecture to generate RGB-D data conditioned on class labels. In the architecture, two parallel paths were used, one to generate RGB data and the second to synthesize depth map. The output from the two parallel paths is combined to generate RGB-D data. The proposed model is used for video generation at 30 fps (frames per second). The frame referred here is an RGB-D with the spatial resolution of 512 × 512.
In contrast to the conventional RGB cameras, Near-infrared (NIR) spectroscopy provides images with rich information concerning the biological process of plants. However, NIR spectroscopy is a costly affair and produces low-resolution (LR) images. In this context, recently deep learning-based methods have been proposed in computer vision. In addition, the development of phenomics facilities has facilitated the generation of large plant data necessary for the utilization of these deep learning-based methods. Motivated by these developments, we propose a novel attention-based pix-to-pix generative adversarial network (GAN) followed by a superresolution (SR) module to generate high-resolution (HR) NIR images from corresponding RGB images. An experiment including extraction of phenotypic data based on HR NIR images has also been conducted to evaluate its efficacy from an agricultural perspective. Our proposed architecture achieved state-of-the-art performance in terms of MRAE and RMSE on the Wheat plant multi-modality dataset.
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