Example-Based Image Quality Enhancement

Owing to the rapid development of digital media technology, advanced media capture devices have become more and more popular in daily life. Whether it is for Professional animation, film and television production, or a large number of normal users, people want to handle the media material they shot or created for further editing. Due to different situation,these clients offer more specific, optimization needs on effects and efficiency. This is undoubtedly a big challenge for post product tools, one of which is the image quality enhancement. While high-definition television, high-definition projectors and other display devices are bringing large amount of high-resolution screen, most general resolution image requires some adjustments in order to be better displayed on these devices. As for some cases, the network speed or storage capacity is limited, so images need to be compressed before transmission. If one can provide a method for enhancing the quality of the limited media information, such as decoding technology, it will have a very promising market. Other related aspects in application include satellite imaging, electronic map zoom, medical assistance, post-film special effects and so on. All of these applications related to prospects for image and video quality enhancement, which is also a great promotion for the super-resolution technology.Inspired of some example-based synthesis technologies, this paper brings up an implement using a series of examples to aid quality adjustment process. These representative examples can be processed to provide additional information beyond the source image. All these example images can be specified by the user, but also directly acquired from the source input image itself on its own self-similarity. After that, for the purpose of enhancing the original image quality, we need to select patches from the examples which best match the image similarity and higher-resolution texture elements. And then, use these patches to replace the original image in certain regions. What's more, the relationship between exemplars can be set to different scales. And if appropriate level structure and topology of the example are provide, you can get better post-treatment effect.In this paper, we use the input image itself or additional images as example in order to improve the quality of media material. Compared with traditional methods, the key is to bring in additional high-frequency information. Take advantage of these hierarchical example information, we can further enhance magnification, optimizing effect of the image enlarged. In this algorithm, we combine the advantages of upsampling and texture synthesis by adding random jitter to obtain better synthesis results. In addition, this paper also accelerated main step by using the GPU parallel computing algorithm so that users can get optimize results in a very short time.