| Summary: | 碩士 === 國立成功大學 === 電機工程學系 === 104 === The objective of single image super-resolution (SR) is to restore a visually pleasing high-resolution (HR) image from a single low-resolution (LR) input. SR reconstruction is an effective signal recovery technique that produces high quality images from low-cost imaging systems (e.g., webcams or mobile phones) and limited environmental conditions (e.g., security surveillance or remote sensing imaging). There are many method nowadays, and the example-learning-based approach is faster than others and restore the detail by using training dataset. It uses linear regression to find the mapping functions for transforming images, but the problem is that storage requirement is large.
In this thesis, we focus on the compression of mapping function to improve applicability of mobile device. This work presents local multi-gradient level pattern (LMGP) to describe the patches, and mapping function can be classified to different image frequency. This thesis compresses storage space remaining original image quality by giving different quantization to values in mapping function.
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