消除图像伪轮廓的各向异性自适应滤波

摘要：目的：图像反差增强、重复量化、有损压缩等操作容易造成伪轮廓瑕疵，使原本平滑的区域呈现不真实的亮度和颜色跳变，损害图像质量。针对这一问题提出一种各向异性自适应滤波方法，用于消除伪轮廓．方法：首先检测图像中的边缘和平坦区，若边缘位于平坦区域则判定其为伪轮廓，得到一幅伪轮廓分布图．对伪轮廓上每一点计算两个特性：伪轮廓走向和分布密度，量化为8个方向和6种尺度，据此确定不同方向特性和不同尺度的滤波参数，选择相应的滤波器．为保护目标边缘不受损伤，在含有伪轮廓的图像中提取强度超过指定阈值的边缘，对其进行膨胀生成模板用以屏蔽滤波效果．结果：该方法能有效消除伪轮廓并保护真实边缘不受损伤。实验中采用峰值信噪比（PSNR）和结构相似度（SSIM）评估图像质量，结果表明，各向异性自适应滤波器特性优于其他方法．结论：消除伪轮廓的自适应图像滤波方法能消除因过度增强或不当量化造成的伪轮廓瑕疵，并保留真实边缘，提高图像的视觉质量.

False contour suppression with anisotropic adaptive filtering

Objective Image processing operations, such as contrast enhancement,re-quantization and compression often produce false contours in the image,featured by unrealistic edges in areas that are actually smooth,which would damage the image quality. To suppress false contours and improve the image quality, we propose an anisotropic adaptive filtering technique based on an analysis of local characteristics of the image edges and false contours. Method Edges are detected using the Canny operator,and the flat areas are identified. Edges in smooth areas are judged as false contours. A map of false contours can be obtained. The direction and density of false contours are then computed to provide a basis for selecting proper filtering parameters. The contour directions are quantized to eight angles,and the scales of the filtering kernels are set to six different values according to the density of the contours. To preserve real edges and avoid unwanted blurring of fine details,edges of sufficient strength are extracted and dilated to form a protection mask. Result The method can effectively reduce false contour artifacts and preserve fine details in the image. Experimental results show that the results are better than those of other methods in terms of peak signal-to-noise ratio (PSNR)and structural similarity index (SSIM). Conclusion The proposed adaptive filtering algorithm can remove false contours in images due to excessive enhancement or improper quantization while keeping true edges and fine details intact to improve the images' visual quality.