Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A scale-based forward-and-backward diffusion process for adaptive image enhancement and denoising | Wang et al. EURASIP Journal on Advances in Signal Processing 2011 2011 22 http content 2011 1 22 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access A scale-based forward-and-backward diffusion process for adaptive image enhancement and denoising Yi Wang1 Ruiqing Niu1 Liangpei Zhang2 Ke Wu1 and Hichem Sahli3 Abstract This work presents a scale-based forward-and-backward diffusion SFABD scheme. The main idea of this scheme is to perform local adaptive diffusion using local scale information. To this end we propose a diffusivity function based on the Minimum Reliable Scale MRS of Elder and Zucker IEEE Trans. Pattern Anal. Mach. Intell. 20 7 699716 1998 to detect the details of local structures. The magnitude of the diffusion coefficient at each pixel is determined by taking into account the local property of the image through the scales. A scale-based variable weight is incorporated into the diffusivity function for balancing the forward and backward diffusion. Furthermore as numerical scheme we propose a modification of the Perona-Malik scheme IEEE Trans. Pattern Anal. Mach. Intell. 12 7 629-639 1990 by incorporating edge orientations. The article describes the main principles of our method and illustrates image enhancement results on a set of standard images as well as simulated medical images together with qualitative and quantitative comparisons with a variety of anisotropic diffusion schemes. Keywords Image enhancement Partial differential equation Forward-and-backward diffusion Scale 1. Introduction Different attributes such as noise due to image acquisition quantization compression and transmission blur or artefacts can influence the perceived quality of digital images 1 and requires post-processing such as image smoothing and sharpening steps for further image analysis including image segmentation feature extraction classification and recognition. In order to reduce noise while preserving .
