A Sharpness Enhancment Algorithm with Adaptive Acutance Compensation

• Main Authors: Kang-Ming Li, 李康銘
• Other Authors: Shen-Chuan Tai
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/08045479300707259854

碩士 === 國立成功大學 === 電腦與通信工程研究所 === 97 === Sharpness enhancement improves perceptive experience to digital images. Digital image sources have different kind of blurriness property because of record devices, lossy compression, or upscaling process, thus affecting our visual experience from one image to another. In this thesis, we propose ad adaptive sharpness and enhancement algorithm as an automatic post-process stage for natural image and video. The proposed algorithm intends to increase perspective experience by adjusting acutance and contrast while keeping the original average luminance. The proposed approach consists of two stages: sharpness and histogram enhancement stage. First, In the sharpness stage, overall acutance of an image/video will be detected through a no-reference sharpness metric, and then, a sharpness enhancement filter will be applied based on this acutance metric. The sharpness filter is tried integrating the concept of Human Visual System (HVS) for better perceptual results. The acutance of processed image from different sources become more consistent according to this stage. Second, In the histogram stage, it adjusts the global histogram by a mean-intensity based weighted histogram equalization, which exposes more image detail on originals after enhancing. This is done by a histogram-based weighting mechanic, which not only emphasizes less probability gray-level bins but consider original mean intensity to eliminate the washed-out and intensity-shift phenomenon that often occurs in histogram equalization process. More stable result in mean-intensity variation can be obtained. This is useful to video enhancement. It also introduces a ``contrast refinement' block, which references original pictures to recover the detail lose that happens on brighter and darker region after enhancement. Experiment results indicate our adaptive algorithm is flexible to several different test sequences, obtaining perceptually satisfied results, and outperforming previous sharpness enhancement algorithms.