Jung Cheolkon, Jiao Licheng
Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China.
Opt Express. 2010 Mar 29;18(7):7138-49. doi: 10.1364/OE.18.007138.
This paper provides a novel Bayesian deringing method to reduce ringing artifacts caused by image interpolation and JPEG compression. To remove the ringing artifacts, the proposed method uses a Bayesian framework based on a SGLI (spatial-gradient-local-inhomogeneity) prior. The SGLI prior employs two complementary discontinuity measures: spatial gradient and local inhomogeniety. The spatial gradient measure effectively detects strong edge components in images. In addition, the local inhomogeniety measure successfully detects locations of the significant discontinuities by taking uniformity of small regions into consideration. The two complementary measures are elaborately combined to create prior probabilities of the Bayesian deringing framework. Thus, the proposed deringing method can effectively preserve the significant discontinuities such as textures of objects as well as the strong edge components in images while reducing the ringing artifacts. Experimental results show that the proposed deringing method achieves average PSNR gains of 0.09 dB in image interpolation artifact reduction and 0.21 dB in JPEG compression artifact reduction.
本文提出了一种新颖的贝叶斯去振铃方法，以减少由图像插值和JPEG压缩引起的振铃伪影。为了去除振铃伪影，该方法使用了基于空间梯度局部不均匀性（SGLI）先验的贝叶斯框架。SGLI先验采用了两种互补的不连续性度量：空间梯度和局部不均匀性。空间梯度度量有效地检测图像中的强边缘分量。此外，局部不均匀性度量通过考虑小区域的均匀性成功检测到显著不连续性的位置。这两种互补度量被精心组合以创建贝叶斯去振铃框架的先验概率。因此，所提出的去振铃方法在减少振铃伪影的同时，可以有效地保留诸如物体纹理等显著不连续性以及图像中的强边缘分量。实验结果表明，所提出的去振铃方法在减少图像插值伪影方面平均PSNR增益为0.09 dB，在减少JPEG压缩伪影方面平均PSNR增益为0.21 dB。
