5 research outputs found
Constrained low-rank quaternion approximation for color image denoising by bilateral random projections
In this letter, we propose a novel low-rank quaternion approximation (LRQA)
model by directly constraining the quaternion rank prior for effectively
removing the noise in color images. The LRQA model treats the color image
holistically rather than independently for the color space components, thus it
can fully utilize the high correlation among RGB channels. We design an
iterative algorithm by using quaternion bilateral random projections (Q-BRP) to
efficiently optimize the proposed model. The main advantage of Q-BRP is that
the approximation of the low-rank quaternion matrix can be obtained quite
accurately in an inexpensive way. Furthermore, color image denoising is further
based on nonlocal self-similarity (NSS) prior. The experimental results on
color image denoising illustrate the effectiveness and superiority of the
proposed method
A hue-preserving tone mapping scheme based on constant-hue plane without gamut problem
We propose a novel hue-preserving tone mapping scheme. Various tone mapping
operations have been studied so far, but there are very few works on color
distortion caused in image tone mapping. First, LDR images produced from HDR
ones by using conventional tone mapping operators (TMOs) are pointed out to
have some distortion in hue values due to clipping and rounding quantization
processing. Next,we propose a novel method which allows LDR images to have the
same maximally saturated color values as those of HDR ones. Generated LDR
images by the proposed method have smaller hue degradation than LDR ones
generated by conventional TMOs. Moreover, the proposed method is applicable to
any TMOs. In an experiment, the proposed method is demonstrated not only to
produce images with small hue degradation but also to maintain well-mapped
luminance, in terms of three objective metrics: TMQI, hue value in CIEDE2000,
and the maximally saturated color on the constant-hue plane in the RGB color
space
Hue-Correction Scheme Considering Non-Linear Camera Response for Multi-Exposure Image Fusion
We propose a novel hue-correction scheme for multi-exposure image fusion
(MEF). Various MEF methods have so far been studied to generate higher-quality
images. However, there are few MEF methods considering hue distortion unlike
other fields of image processing, due to a lack of a reference image that has
correct hue. In the proposed scheme, we generate an HDR image as a reference
for hue correction, from input multi-exposure images. After that, hue
distortion in images fused by an MEF method is removed by using hue information
of the HDR one, on the basis of the constant-hue plane in the RGB color space.
In simulations, the proposed scheme is demonstrated to be effective to correct
hue-distortion caused by conventional MEF methods. Experimental results also
show that the proposed scheme can generate high-quality images, regardless of
exposure conditions of input multi-exposure images
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation
In color image processing, image completion aims to restore missing entries
from the incomplete observation image. Recently, great progress has been made
in achieving completion by approximately solving the rank minimization problem.
In this paper, we utilize a novel quaternion matrix logarithmic norm to
approximate rank under the quaternion matrix framework. From one side, unlike
the traditional matrix completion method that handles RGB channels separately,
the quaternion-based method is able to avoid destroying the structure of images
via putting the color image in a pure quaternion matrix. From the other side,
the logarithmic norm induces a more accurate rank surrogate. Based on the
logarithmic norm, we take advantage of not only truncated technique but also
factorization strategy to achieve image restoration. Both strategies are
optimized based on the alternating minimization framework. The experimental
results demonstrate that the use of logarithmic surrogates in the quaternion
domain is more superior in solving the problem of color images completion.Comment: 35 pages, 7 figure
Quaternion-based bilinear factor matrix norm minimization for color image inpainting
As a new color image representation tool, quaternion has achieved excellent
results in the color image processing, because it treats the color image as a
whole rather than as a separate color space component, thus it can make full
use of the high correlation among RGB channels. Recently, low-rank quaternion
matrix completion (LRQMC) methods have proven very useful for color image
inpainting. In this paper, we propose three novel LRQMC methods based on three
quaternion-based bilinear factor (QBF) matrix norm minimization models.
Specifically, we define quaternion double Frobenius norm (Q-DFN), quaternion
double nuclear norm (Q-DNN) and quaternion Frobenius/nuclear norm (Q-FNN), and
then show their relationship with quaternion-based matrix Schatten-p (Q-
Schatten-p ) norm for certain p values. The proposed methods can avoid
computing quaternion singular value decompositions (QSVD) for large quaternion
matrices, and thus can effectively reduce the calculation time compared with
existing (LRQMC) methods. The experimental results demonstrate the superior
performance of the proposed methods over some state-of-the-art low-rank
(quaternion) matrix completion methods