Image denoising using patch ordering and 3D transformation of patches

A novel image denoising algorithm which is based on the ordering of noisy image patches into a 3D array and the application of 3D transformations on this image dependent patch cube is proposed. For a given noisy image, the authors extract all the patches with overlaps. Then, they order these patches according to a predefined similarity measure. After reordering, a possibly separable 3D transformation is applied to the reordered 3D patch cube. The transform domain coefficients are thresholded using a suitably calculated thresholding parameter. Afterwards, the proper 3D inverse transformation is applied to these coefficients. The final denoised image is generated by repositioning the processed patches to their original locations on the image canvas. The developed algorithm presents a novel and efficient combination of patch ordering and 3D transformations. The forward analysis transform as defined by this complex procedure can get restated as the application of a single tight frame. This tight frame depends on the noisy image under consideration. This novel, image dependent forward operator which employs 3D transforms results in improved denoising performance. The experimental results indicate that the proposed algorithm achieves state-of-the-art denoising results with complexity comparable to competing methods.