Dataset Information

Elimination of Image Saturation Effects on Multifractal Statistics Using the 2D WTMM Method.

ABSTRACT: Imaging artifacts such as image saturation can restrict the computational analysis of medical images. Multifractal analyses are typically restricted to self-affine, everywhere singular, surfaces. Image saturation regions in these rough surfaces rob them of these core properties, and their exclusion decreases the statistical power of clinical analyses. By adapting the powerful 2D Wavelet Transform Modulus Maxima (WTMM) multifractal method, we developed a strategy where the image can be partitioned according to its localized response to saturated regions. By eliminating the contribution from those saturated regions to the partition function calculations, we show that the estimation of the multifractal statistics can be correctly calculated even with image saturation levels up to 20% (where 20% is the number of saturated pixels over the total number of pixels in the image).

SUBMITTER: Juybari J

PROVIDER: S-EPMC9273936 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Elimination of Image Saturation Effects on Multifractal Statistics Using the 2D WTMM Method.

Juybari Jeremy J Khalil Andre A

Frontiers in physiology 20220628

Imaging artifacts such as image saturation can restrict the computational analysis of medical images. Multifractal analyses are typically restricted to self-affine, everywhere singular, surfaces. Image saturation regions in these rough surfaces rob them of these core properties, and their exclusion decreases the statistical power of clinical analyses. By adapting the powerful 2D Wavelet Transform Modulus Maxima (WTMM) multifractal method, we developed a strategy where the image can be partitione ...[more]

PMID: 35837020

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Project description:One of the key challenges in facial recognition is multi-view face synthesis from a single face image. The existing generative adversarial network (GAN) deep learning methods have been proven to be effective in performing facial recognition with a set of pre-processing, post-processing and feature representation techniques to bring a frontal view into the same position in-order to achieve high accuracy face identification. However, these methods still perform relatively weak in generating high quality frontal-face image samples under extreme face pose scenarios. The novel framework architecture of the two-pathway generative adversarial network (TP-GAN), has made commendable progress in the face synthesis model, making it possible to perceive global structure and local details in an unsupervised manner. More importantly, the TP-GAN solves the problems of photorealistic frontal view synthesis by relying on texture details of the landmark detection and synthesis functions, which limits its ability to achieve the desired performance in generating high-quality frontal face image samples under extreme pose. We propose, in this paper, a landmark feature-based method (LFM) for robust pose-invariant facial recognition, which aims to improve image resolution quality of the generated frontal faces under a variety of facial poses. We therefore augment the existing TP-GAN generative global pathway with a well-constructed 2D face landmark localization to cooperate with the local pathway structure in a landmark sharing manner to incorporate empirical face pose into the learning process, and improve the encoder-decoder global pathway structure for better representation of facial image features by establishing robust feature extractors that select meaningful features that ease the operational workflow toward achieving a balanced learning strategy, thus significantly improving the photorealistic face image resolution. We verify the effectiveness of our proposed method on both Multi-PIE and FEI datasets. The quantitative and qualitative experimental results show that our proposed method not only generates high quality perceptual images under extreme poses but also significantly improves upon the TP-GAN results.

Dataset Information

Elimination of Image Saturation Effects on Multifractal Statistics Using the 2D WTMM Method.

Publications

Elimination of Image Saturation Effects on Multifractal Statistics Using the 2D WTMM Method.

Similar Datasets

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