58,367 research outputs found
Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data
With recent deep learning based approaches showing promising results in
removing noise from images, the best denoising performance has been reported in
a supervised learning setup that requires a large set of paired noisy images
and ground truth for training. The strong data requirement can be mitigated by
unsupervised learning techniques, however, accurate modelling of images or
noise variance is still crucial for high-quality solutions. The learning
problem is ill-posed for unknown noise distributions. This paper investigates
the tasks of image denoising and noise variance estimation in a single, joint
learning framework. To address the ill-posedness of the problem, we present
deep variation prior (DVP), which states that the variation of a properly
learnt denoiser with respect to the change of noise satisfies some smoothness
properties, as a key criterion for good denoisers. Building upon DVP, an
unsupervised deep learning framework, that simultaneously learns a denoiser and
estimates noise variances, is developed. Our method does not require any clean
training images or an external step of noise estimation, and instead,
approximates the minimum mean squared error denoisers using only a set of noisy
images. With the two underlying tasks being considered in a single framework,
we allow them to be optimised for each other. The experimental results show a
denoising quality comparable to that of supervised learning and accurate noise
variance estimates
Sparsity Based Poisson Denoising with Dictionary Learning
The problem of Poisson denoising appears in various imaging applications,
such as low-light photography, medical imaging and microscopy. In cases of high
SNR, several transformations exist so as to convert the Poisson noise into an
additive i.i.d. Gaussian noise, for which many effective algorithms are
available. However, in a low SNR regime, these transformations are
significantly less accurate, and a strategy that relies directly on the true
noise statistics is required. A recent work by Salmon et al. took this route,
proposing a patch-based exponential image representation model based on GMM
(Gaussian mixture model), leading to state-of-the-art results. In this paper,
we propose to harness sparse-representation modeling to the image patches,
adopting the same exponential idea. Our scheme uses a greedy pursuit with
boot-strapping based stopping condition and dictionary learning within the
denoising process. The reconstruction performance of the proposed scheme is
competitive with leading methods in high SNR, and achieving state-of-the-art
results in cases of low SNR.Comment: 13 pages, 9 figure
Terahertz Security Image Quality Assessment by No-reference Model Observers
To provide the possibility of developing objective image quality assessment
(IQA) algorithms for THz security images, we constructed the THz security image
database (THSID) including a total of 181 THz security images with the
resolution of 127*380. The main distortion types in THz security images were
first analyzed for the design of subjective evaluation criteria to acquire the
mean opinion scores. Subsequently, the existing no-reference IQA algorithms,
which were 5 opinion-aware approaches viz., NFERM, GMLF, DIIVINE, BRISQUE and
BLIINDS2, and 8 opinion-unaware approaches viz., QAC, SISBLIM, NIQE, FISBLIM,
CPBD, S3 and Fish_bb, were executed for the evaluation of the THz security
image quality. The statistical results demonstrated the superiority of Fish_bb
over the other testing IQA approaches for assessing the THz image quality with
PLCC (SROCC) values of 0.8925 (-0.8706), and with RMSE value of 0.3993. The
linear regression analysis and Bland-Altman plot further verified that the
Fish__bb could substitute for the subjective IQA. Nonetheless, for the
classification of THz security images, we tended to use S3 as a criterion for
ranking THz security image grades because of the relatively low false positive
rate in classifying bad THz image quality into acceptable category (24.69%).
Interestingly, due to the specific property of THz image, the average pixel
intensity gave the best performance than the above complicated IQA algorithms,
with the PLCC, SROCC and RMSE of 0.9001, -0.8800 and 0.3857, respectively. This
study will help the users such as researchers or security staffs to obtain the
THz security images of good quality. Currently, our research group is
attempting to make this research more comprehensive.Comment: 13 pages, 8 figures, 4 table
- …