Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
| Hi-index | 0.00 |
State-of-the-art image denoising algorithms attempt to recover natural image signals from their noisy observations, such that the statistics of the denoised image follow the statistical regularities of natural images. One aspect generally missing in these approaches is that the properties of the residual image (defined as the difference between the noisy observation and the denoised image) have not been well exploited. Here we demonstrate the usefulness of residual images in image denoising. In particular, we show that well-known full-reference image quality measures such as the mean-squared-error and the structural similarity index can be estimated from the residual image without the reference image. We also propose a procedure that has the potential to enhance the image quality of given image denoising algorithms.