9,694 research outputs found
No-reference Image Denoising Quality Assessment
A wide variety of image denoising methods are available now. However, the
performance of a denoising algorithm often depends on individual input noisy
images as well as its parameter setting. In this paper, we present a
no-reference image denoising quality assessment method that can be used to
select for an input noisy image the right denoising algorithm with the optimal
parameter setting. This is a challenging task as no ground truth is available.
This paper presents a data-driven approach to learn to predict image denoising
quality. Our method is based on the observation that while individual existing
quality metrics and denoising models alone cannot robustly rank denoising
results, they often complement each other. We accordingly design denoising
quality features based on these existing metrics and models and then use Random
Forests Regression to aggregate them into a more powerful unified metric. Our
experiments on images with various types and levels of noise show that our
no-reference denoising quality assessment method significantly outperforms the
state-of-the-art quality metrics. This paper also provides a method that
leverages our quality assessment method to automatically tune the parameter
settings of a denoising algorithm for an input noisy image to produce an
optimal denoising result.Comment: 17 pages, 41 figures, accepted by Computer Vision Conference (CVC)
201
Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment Loss
Recently, denoising methods based on supervised learning have exhibited
promising performance. However, their reliance on external datasets containing
noisy-clean image pairs restricts their applicability. To address this
limitation, researchers have focused on training denoising networks using
solely a set of noisy inputs. To improve the feasibility of denoising
procedures, in this study, we proposed a single-image self-supervised learning
method in which only the noisy input image is used for network training. Gated
convolution was used for feature extraction and no-reference image quality
assessment was used for guiding the training process. Moreover, the proposed
method sampled instances from the input image dataset using Bernoulli sampling
with a certain dropout rate for training. The corresponding result was produced
by averaging the generated predictions from various instances of the trained
network with dropouts. The experimental results indicated that the proposed
method achieved state-of-the-art denoising performance on both synthetic and
real-world datasets. This highlights the effectiveness and practicality of our
method as a potential solution for various noise removal tasks.Comment: Technical report and supplemantry materials are combined into one
paper. - Technical report: Page 1~7 - Supplemantry materials : Page 8~1
DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD
In this paper we propose automatic image denoising method based on Hermite functions (HeNLM). It is an extension of non-local means (NLM) algorithm. Differences between small image blocks (patches) are replaced by differences between feature vectors thus reducing computational complexity. The features are calculated in coordinate system connected with image gradient and are invariant to patch rotation. HeNLM method depends on the parameter that controls filtering strength. To chose automatically this parameter we use a no-reference denoising quality assessment method. It is based on Hessian matrix analysis. We compare the proposed method with full-reference methods using PSNR metrics, SSIM metrics, and its modifications MSSIM and CMSC. Image databases TID, DRIVE, BSD, and a set of dermatological immunofluorescence microscopy images were used for the tests. It was found that more perceptual CMSC and MSSIM metrics give worse correspondence than SSIM and PSNR to the results of information preservation by the non-reference image denoising
Statistical evaluation of visual quality metrics for image denoising
This paper studies the problem of full reference visual quality assessment of
denoised images with a special emphasis on images with low contrast and
noise-like texture. Denoising of such images together with noise removal often
results in image details loss or smoothing. A new test image database, FLT,
containing 75 noise-free "reference" images and 300 filtered ("distorted")
images is developed. Each reference image, corrupted by an additive white
Gaussian noise, is denoised by the BM3D filter with four different values of
threshold parameter (four levels of noise suppression). After carrying out a
perceptual quality assessment of distorted images, the mean opinion scores
(MOS) are obtained and compared with the values of known full reference quality
metrics. As a result, the Spearman Rank Order Correlation Coefficient (SROCC)
between PSNR values and MOS has a value close to zero, and SROCC between values
of known full-reference image visual quality metrics and MOS does not exceed
0.82 (which is reached by a new visual quality metric proposed in this paper).
The FLT dataset is more complex than earlier datasets used for assessment of
visual quality for image denoising. Thus, it can be effectively used to design
new image visual quality metrics for image denoising.Comment: Submitted to ICASSP 201
A statistical reduced-reference method for color image quality assessment
Although color is a fundamental feature of human visual perception, it has
been largely unexplored in the reduced-reference (RR) image quality assessment
(IQA) schemes. In this paper, we propose a natural scene statistic (NSS)
method, which efficiently uses this information. It is based on the statistical
deviation between the steerable pyramid coefficients of the reference color
image and the degraded one. We propose and analyze the multivariate generalized
Gaussian distribution (MGGD) to model the underlying statistics. In order to
quantify the degradation, we develop and evaluate two measures based
respectively on the Geodesic distance between two MGGDs and on the closed-form
of the Kullback Leibler divergence. We performed an extensive evaluation of
both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID
2008 benchmark and the FRTV Phase I validation process. Experimental results
demonstrate the effectiveness of the proposed framework to achieve a good
consistency with human visual perception. Furthermore, the best configuration
is obtained with CIELAB color space associated to KLD deviation measure
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