Context-Constrained Hallucination for Image Super-Resolution

This paper proposes a context-constrained hallucination approach for image super-resolution. Through building a training set of high-resolution/low-resolution image segment pairs, the high-resolution pixel is hallucinated from its texturally similar segments which are retrieved from the training set by texture similarity. Given the discrete hallucinated examples, a continuous energy function is designed to enforce the fidelity of high-resolution image to low-resolution input and the constraints imposed by the hallucinated examples and the edge smoothness prior. The reconstructed high-resolution image is sharp with minimal artifacts both along the edges and in the textural regions.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Jian Sun, Jiejie Zhu, Marshall Tappen
• Keywords: Computer Vision, Context-constrained Hallucination Approach, CVPR 2010, Discrete Hallucinated Examples, Training Set