Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation

Grossi, Giuliano; Lanzarotti, Raffaella; Lin, Jianyi

doi:10.1371/journal.pone.0169663

Cited by 16 publications

(3 citation statements)

References 37 publications

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“…The processing effects of the three algorithms were compared when the noise ρ was 10, 30, 50, and 70, respectively. The results showed that the PSNR and SSIM values of N-KSVD dictionary after denoising were both greater than those of DCT dictionary, and the differences were statistically significant, which indicated that N-KSVD dictionary had the best denoising effect, in line with the research results of Grossi et al [ 30 ]. Subsequently, N-KSVD dictionary was used in the diagnosis of patients, and the test group and the control group were compared for basic information.…”

Section: Discussionsupporting

confidence: 85%

Dictionary Learning-Based Ultrasound Image Combined with Gastroscope for Diagnosis of Helicobacter pylori-Caused Gastrointestinal Bleeding

Diao

Zhang

2021

Computational and Mathematical Methods in Medicine

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The study is aimed at evaluating the application value of ultrasound combined with gastroscopy in diagnosing gastrointestinal bleeding (GIB) caused by Helicobacter pylori (HP). An ultrasound combined with a gastroscopy diagnostic model based on improved K -means Singular Value Decomposition (N-KSVD) was proposed first. 86 patients with Peptic ulcer (PU) and GIB admitted to our Hospital were selected and defined as the test group, and 86 PU patients free of GIB during the same period were selected as the control group. The two groups were observed for clinical manifestations and HP detection results. The results showed that when the noise ρ was 10, 30, 50, and 70, the Peak Signal to Noise Ratio (PSNR) values of N-KSVD dictionary after denoising were 35.55, 30.47, 27.91, and 26.08, respectively, and the structure similarity index measure (SSIM) values were 0.91, 0.827, 0.763, and 0.709, respectively. Those were greater than those of DCT dictionary and Global dictionary and showed statistically significant differences versus the DCT dictionary ( P < 0.05 ). In the test group, there were 60 HP-positives and 26 HP-negatives, and there was significant difference in the numbers of HP-positives and HP-negatives ( P < 0.05 ), but no significant difference in gender and age ( P > 0.05 ). Of the subjects with abdominal pain, HP-positives accounted for 59.02% and HP-negatives accounted for 37.67%, showing significant differences ( P < 0.05 ). Finally, the size of the ulcer lesion in HP-positives and HP-negatives was compared. It was found that 71.57% of HP-positives had ulcers with a diameter of 0-1 cm, and 28.43% had ulcers with a diameter of ≥1 cm. Compared with HP-negatives, the difference was statistically significant ( P < 0.05 ). In conclusion, N-KSVD-based ultrasound combined with gastroscopy demonstrated good denoising effects and was effective in the diagnosis of GIB caused by HP.

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Section: Discussionsupporting

confidence: 85%

Dictionary Learning-Based Ultrasound Image Combined with Gastroscope for Diagnosis of Helicobacter pylori-Caused Gastrointestinal Bleeding

Diao

Zhang

2021

Computational and Mathematical Methods in Medicine

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“…Recently, sparse representation has been extensively used for applications such as compressed sensing, reconstruction, and de-noising of medical images [46]. However, there are limited studies for sparse bio-signals de-noising [47]. These methods assume that the natural signal are sparse on to either a fixed dictionary like the Fourier and wavelet transform or a learned dictionary [48].…”

Section: Introductionmentioning

confidence: 99%

Dictionary learning technique enhances signal in LED-based photoacoustic imaging

Farnia

Najafzadeh

Hariri

et al. 2020

Biomed. Opt. Express

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There has been growing interest in low-cost light sources such as light-emitting diodes (LEDs) as an excitation source in photoacoustic imaging. However, LED-based photoacoustic imaging is limited by low signal due to low energy per pulse-the signal is easily buried in noise leading to low quality images. Here, we describe a signal de-noising approach for LED-based photoacoustic signals based on dictionary learning with an alternating direction method of multipliers. This signal enhancement method is then followed by a simple reconstruction approach delay and sum. This approach leads to sparse representation of the main components of the signal. The main improvements of this approach are a 38% higher contrast ratio and a 43% higher axial resolution versus the averaging method but with only 4% of the frames and consequently 49.5% less computational time. This makes it an appropriate option for real-time LED-based photoacoustic imaging.

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“…The feature space is obtained employing deep features coupled with the linear discriminant analysis, while the concise model is derived adopting the method of optimal directions (MOD) [13], which has proved to be very efficient for low-dimensional input data. The benefits of this approach is that, contrarily to generic learning algorithms [14], the label consistency between dictionary atoms and training data is maintained, allowing the direct application of the classification stage based on majority voting (a demo code is available on the website: ).…”

Section: Introductionmentioning

confidence: 99%

Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features

Cuculo

D’Amelio

Grossi

et al. 2019

Sensors

Self Cite

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Face recognition using a single reference image per subject is challenging, above all when referring to a large gallery of subjects. Furthermore, the problem hardness seriously increases when the images are acquired in unconstrained conditions. In this paper we address the challenging Single Sample Per Person (SSPP) problem considering large datasets of images acquired in the wild, thus possibly featuring illumination, pose, face expression, partial occlusions, and low-resolution hurdles. The proposed technique alternates a sparse dictionary learning technique based on the method of optimal direction and the iterative sans-serifℓ0-norm minimization algorithm called k-LiMapS. It works on robust deep-learned features, provided that the image variability is extended by standard augmentation techniques. Experiments show the effectiveness of our method against the hardness introduced above: first, we report extensive experiments on the unconstrained LFW dataset when referring to large galleries up to 1680 subjects; second, we present experiments on very low-resolution test images up to 8×8 pixels; third, tests on the AR dataset are analyzed against specific disguises such as partial occlusions, facial expressions, and illumination problems. In all the three scenarios our method outperforms the state-of-the-art approaches adopting similar configurations.

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Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation

Cited by 16 publications

References 37 publications

Dictionary Learning-Based Ultrasound Image Combined with Gastroscope for Diagnosis of Helicobacter pylori-Caused Gastrointestinal Bleeding

Dictionary Learning-Based Ultrasound Image Combined with Gastroscope for Diagnosis of Helicobacter pylori-Caused Gastrointestinal Bleeding

Dictionary learning technique enhances signal in LED-based photoacoustic imaging

Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features

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