William Jino Hans, Venkateswaran N, Narayanan Srinath, Ramachandran Sandeep
Department of ECE, SSN College of Engineering, Chennai, Tamil Nadu 603 110, India.
ScientificWorldJournal. 2016;2016:8306342. doi: 10.1155/2016/8306342. Epub 2016 Mar 15.
A selfie is typically a self-portrait captured using the front camera of a smartphone. Most state-of-the-art smartphones are equipped with a high-resolution (HR) rear camera and a low-resolution (LR) front camera. As selfies are captured by front camera with limited pixel resolution, the fine details in it are explicitly missed. This paper aims to improve the resolution of selfies by exploiting the fine details in HR images captured by rear camera using an example-based super-resolution (SR) algorithm. HR images captured by rear camera carry significant fine details and are used as an exemplar to train an optimal matrix-value regression (MVR) operator. The MVR operator serves as an image-pair priori which learns the correspondence between the LR-HR patch-pairs and is effectively used to super-resolve LR selfie images. The proposed MVR algorithm avoids vectorization of image patch-pairs and preserves image-level information during both learning and recovering process. The proposed algorithm is evaluated for its efficiency and effectiveness both qualitatively and quantitatively with other state-of-the-art SR algorithms. The results validate that the proposed algorithm is efficient as it requires less than 3 seconds to super-resolve LR selfie and is effective as it preserves sharp details without introducing any counterfeit fine details.
自拍照通常是用智能手机的前置摄像头拍摄的自画像。大多数最先进的智能手机都配备了高分辨率（HR）后置摄像头和低分辨率（LR）前置摄像头。由于自拍照是由像素分辨率有限的前置摄像头拍摄的，其中的精细细节明显缺失。本文旨在通过使用基于示例的超分辨率（SR）算法，利用后置摄像头拍摄的HR图像中的精细细节来提高自拍照的分辨率。后置摄像头拍摄的HR图像包含大量精细细节，并用作训练最优矩阵值回归（MVR）算子的示例。MVR算子作为图像对先验，学习LR-HR补丁对之间的对应关系，并有效地用于对LR自拍照图像进行超分辨率处理。所提出的MVR算法避免了图像补丁对的矢量化，并在学习和恢复过程中保留了图像级信息。将所提出的算法与其他最先进的SR算法进行定性和定量评估，以验证其效率和有效性。结果证实，所提出的算法效率很高，因为它对LR自拍照进行超分辨率处理所需时间不到3秒，而且效果很好，因为它保留了清晰的细节，没有引入任何伪造的精细细节。
