5 accepted papers
Under stereo settings, the problems of disparity estimation, stereo magnification and stereo-view synthesis have gathered wide attention. However, the limited image quality brings non-negligible difficulties in developing related applications and becomes the main bottleneck of stereo images. To the…
Single-image super-resolution (SISR) is a very important and classic problem of the computer vision community. Although a lot of SISR methods have been proposed, few studies have been conducted to address the quality assessment of SISR methods. In this paper, we proposed a deep learning based no-ref…
Face hallucination aims to generate a high resolution face from a low resolution one. Generic super resolution methods can not solve this problem well, because human face has a strong structure. With the rapid development of the deep learning technique, some convolutional neural networks (CNNs) mode…
Identifying small size images or small objects is a notoriously challenging problem, as discriminative representations are difficult to learn from the limited information contained in them with poor-quality appearance and unclear object structure. Existing research works usually increase the resolut…
Convolutional neural networks (CNNs) have been widely used in computer vision community. Single image super-resolution (SISR) is a classic computer vision problem, which aims to output a high-resolution image from a low-resolution one. In recent years, CNNs-based SISR methods emerged and achieved a…