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. 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. 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). 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.