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The future of image restoration is likely to be driven by developments in deep learning and artificial intelligence. Convolutional neural networks (CNNs) have shown promising results in various image restoration tasks, including denoising, super-resolution, and inpainting.
Liu's recognition research has focused on fundamental problems such as designing effective loss functions for face matcher learning, integrating identity information across frames, exploring the link between image restoration and recognition in low-quality imagery, and investigating the role of 2D and 3D shapes in recognition. [2]
The journal covers research in computer vision and image understanding, pattern analysis and recognition, machine intelligence, machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition.
A neural radiance field (NeRF) is a method based on deep learning for reconstructing a three-dimensional representation of a scene from two-dimensional images. The NeRF model enables downstream applications of novel view synthesis, scene geometry reconstruction, and obtaining the reflectance properties of the scene.
Jianping Shi, Qiong Yan, Li Xu, Jiaya Jia, "Hierarchical Image Saliency Detection on Extended CSSD" IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015. Jianping Shi, Xin Tao, Li Xu, Jiaya Jia, "Break Ames Room Illusion: Depth from General Single Images" ACM Transactions on Graphics (TOG), (Proc. ACM SIGGRAPH ASIA2015).
Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction , super-resolution , and inpainting .
Digital image restoration and reconstruction. Deep learning neural network-based inpainting can be used for decensoring images. [14] Deep image prior-based techniques can be used for digital image inpainting, where a trained deep learning model is either unavailable or infeasible.
Various deep learning approaches have been proposed to achieve noise reduction [46] and such image restoration tasks. Deep Image Prior is one such technique that makes use of convolutional neural network and is notable in that it requires no prior training data. [47]