Kireeti Bodduna

Patch-based approaches such as 3D block matching (BM3D) and non-local Bayes (NLB) are widely accepted filters for removing Gaussian noise from single-frame images. In this work, we propose three extensions for these filters when there exist multiple frames of the same scene. The first of them employs reference patches on every frame instead of a commonly used single reference frame method, thus utilizing the complete available information. The remaining two techniques use a separable spatio-temporal filter to reduce interactions between dissimilar regions, hence mitigating artifacts. In order to deal with non-registered datasets we combine all our extensions with robust optical flow computation. Two of our proposed multi-frame filters outperform existing extensions on most occasions by a significant margin while also being competitive with a state-of-the-art neural network-based technique. Moreover, one of these two strategies is the fastest among all due to its separable design.

Patch-based approaches such as 3D block matching and non-local Bayes are widely accepted filters for removing Gaussian noise from single-frame images. We propose three extensions for these filters when there exist multiple frames of the same scene. The first of them employs reference patches on every frame instead of a commonly used single-reference frame method, thus utilizing the complete available information. The remaining two techniques use a separable spatiotemporal filter to reduce interactions between dissimilar regions, hence mitigating artifacts. In order to deal with non-registered datasets, we combine all our extensions with robust optical flow computation. Two of our proposed multi-frame filters outperform existing extensions on most occasions by a significant margin while also being competitive with a state-of-the-art neural network-based technique. Moreover, one of these two strategies is the fastest among all due to its separable design.

Leading denoising methods such as 3D block matching (BM3D) are patch-based. However, they can suffer from frequency domain artefacts and require to specify explicit noise models. We present a patch-based method that avoids these drawbacks. It combines a simple and fast patch reordering with a non-linear smoothing. The smoothing rewards both patch and pixel similarities in a multiplicative way. We perform experiments on real world images with additive white Gaussian noise (AWGN), and on electron microscopy data with a more general additive noise model. Our filter outperforms BM3D in 77% of the experiments, with improvements of up to 29% with respect to the mean squared error.

The 3D block matching (BM3D) filter belongs to the state-of-the-art techniques for eliminating additive white Gaussian noise from single-frame images. There exist four multi-frame extensions of BM3D as of today. In this work, we combine these extensions with a variance stabilising transformation (VST) for eliminating Poisson noise. Our evaluation reveals that the extension which retains the original noise model of the noisy images and additionally has a comprehensive connectivity of 2D and temporal image information at both pixel and patch levels, gives the best results. Additionally, we find a surprising change in performance of one the four extensions due to the specific application of the VST. Finally, we also introduce a simple low-pass filtering as a preprocessing step for the best performing extension. This can give rise to a significant additional improvement of 0.94 dB in the output according to the peak signal to noise ratio.

The 3D block matching (BM3D) filter belongs to the state-of-the-art techniques for eliminating additive white Gaussian noise from single-frame images. There exist four multi-frame extensions of BM3D as of today. In this work, we combine these extensions with a variance stabilising transformation (VST) for eliminating Poisson noise. Our evaluation reveals that the extension which retains the original noise model of the noisy images and additionally has a comprehensive connectivity of 2D and temporal image information at both pixel and patch levels, gives the best results. Additionally, we find a surprising change in performance of one the four extensions due to the specific application of the VST. Finally, we also introduce a simple low-pass filtering as a preprocessing step for the best performing extension. This can give rise to a significant additional improvement of 0.94 dB in the output according to the peak signal to noise ratio.

The 3D block matching (BM3D) method is among the state-of-art methods for denoising images corrupted with additive white Gaussian noise. With the help of a novel inter-frame connectivity strategy, we propose an extension of the BM3D method for the scenario where we have multiple images of the same scene. Our proposed extension outperforms all the existing trivial and non-trivial extensions of patch-based denoising methods for multi-frame images. We can achieve a quality difference of as high as 28% over the next best method without using any additional parameters. Our method can also be easily generalised to other similar existing patch-based methods.