In the previous work, the LoG (Laplacian of Gaussian) signal that is the earliest stage output of human visual neural
system was suggested to be useful in image quality assessment (IQA) model design. This work considered that LoG
signal carried crucial structural information of IQA in the position of its zero-crossing and proposed a Non-shift Edge
(NSE) based IQA model. In this study, we focus on another aspect of the properties of the LoG signal, i.e., LoG whitens
the power spectrum of natural images. Here our interest is that: when exposed to unnatural images, specifically distorted
images, how does the HVS whitening this type of signals? In this paper, we first investigate the whitening filter for
natural image and distorted image respectively, and then suggest that the LoG is also a whitening filter for distorted
images to some extent. Based on this fact, we deploy the LOG signal in the task of IQA model design by applying two
very simple distance metrics, i.e., the MSE (mean square error) and the correlation. The proposed models are analyzed
according to the evaluation performance on three subjective databases. The experimental results validate the usability of
the LoG signal in IQA model design and that the proposed models stay in the state-of-the-art IQA models.
Oceans play a significant role in the global carbon cycle and climate change, and the most importantly it is a reservoir for
plenty of protein supply, and at the center of many economic activities. Ocean health is important and can be monitored
by observing different parameters, but the main element is the phytoplankton concentration (chlorophyll–a
concentration) because it is the indicator of ocean productivity. Many methods can be used to estimate chlorophyll–a
(Chl-a) concentration, among them, remote sensing technique is one of the most suitable methods for monitoring the
ocean health locally, regionally and globally with very high temporal resolution.
In this research, long term ocean health monitoring was carried out at the Bay of Bengal considering three facts i.e. i)
very dynamic local weather (monsoon), ii) large number of population in the vicinity of the Bay of Bengal, and iii) the
frequent natural calamities (cyclone and flooding) in and around the Bay of Bengal. Data (ten years: from 2001 to 2010)
from SeaWiFS and MODIS were used. Monthly Chl–a concentration was estimated from the SeaWiFS data using OC4
algorithm, and the monthly sea surface temperature was obtained from the MODIS sea surface temperature (SST) data.
Information about cyclones and floods were obtained from the necessary sources and in-situ Chl–a data was collected
from the published research papers for the validation of Chl-a from the OC4 algorithm. Systematic random sampling was
used to select 70 locations all over the Bay of Bengal for extracting data from the monthly Chl-a and SST maps. Finally
the relationships between different aspects i.e. i) Chl-a and SST, ii) Chl-a and monsoon, iii) Chl-a and cyclones, and iv)
Chl-a and floods were investigated monthly, yearly and for long term (i.e 10 years). Results indicate that SST, monsoon,
cyclone, and flooding can affect Chl-a concentration but the effect of monsoon, cyclone, and flooding is temporal, and
normally reduces over time. However, the effect of SST on Chl-a concentration can't be minimized very quickly
although the change of temperature over this period is not very large.
In lossy image/video encoding, there is a compromise between the number of bits (rate) and the extent of distortion. Bits need to be properly allocated to different sources, such as frames and macro blocks (MBs). Since the human eyes are more sensitive to the difference than the absolute value of signals, the MINMAX criterion suggests to minimizing the maximum distortion of the sources to limit quality fluctuation. There are many works aimed to such constant quality encoding, however, almost all of them focus on the frame layer bit allocation, and use PSNR as the quality index. We suggest that the bit allocation for MBs should also be constrained in the constant quality, and furthermore, perceptual quality indices should be used instead of PSNR. Based on this idea, we propose a multi-pass block-layer bit allocation scheme for quality constrained encoding. The experimental results show that the proposed method can achieve much better encoding performance. Keywords: Bit allocation, block-layer, perceptual quality, constant quality, quality constrained
Measurement of visual quality is of fundamental importance for numerous image and video processing applications. This
paper presented a novel and concise reduced reference (RR) image quality assessment method. Statistics of local binary
pattern (LBP) is introduced as a similarity measure to form a novel RR image quality assessment (IQA) method for the
first time. With this method, first, the test image is decomposed with a multi-scale transform. Second, LBP encoding
maps are extracted for each of subband images. Third, the histograms are extracted from the LBP encoding map to form
the RR features. In this way, image structure primitive information for RR features extraction can be reduced greatly.
Hence, new RR IQA method is formed with only at most 56 RR features. The experimental results on two large scale
IQA databases show that the statistic of LBPs is fairly robust and reliable to RR IQA task. The proposed methods show
strong correlations with subjective quality evaluations.
X-ray scatter leads to erroneous calculations of dual-energy digital mammography (DEDM). The purpose of this work is
to design an algorithmic method for scatter correction in DEDM without extra exposures or lead sheet. The method was
developed based on the knowledge that scatter radiation in mammograms varies slowly spatially and most pixels in
mammograms are non-calcification pixels, and implemented on a commercial full-field digital mammography system
with a phantom of breast tissue equivalent material. The
pinhole-array interpolation scatter correction method was also
implemented on the system. We compared the background dual-energy (DE) calcification signals in the DE calcification
images. Results show that the background signal in the DE calcification image can be reduced. The rms of background
DE calcification image signal of 1105μm with scatter-uncorrected data was reduced to 187μm and 253μm after scatter
correction, using our algorithmic method and pinhole-array interpolation method, respectively. The range of background
DE calcification signals using scatter-uncorrected data was reduced by ~80% with scatter-corrected data using
algorithmic method. The proposed algorithmic scatter correction method is effective; it has similar or even better
performance than pinhole-array interpolation method in scatter correction for DEDM.
The reduced reference (RR) image quality assessment (IQA) has been attracting much attention from researchers for its
loyalty to human perception and flexibility in practice. A promising RR metric should be able to predict the perceptual
quality of an image accurately while using as few features as possible. In this paper, a novel RR metric is presented,
whose novelty lies in two aspects. Firstly, it measures the image redundancy by calculating the so-called Sub-image
Similarity (SIS), and the image quality is measured by comparing the SIS between the reference image and the test
image. Secondly, the SIS is computed by the ratios of NSE (Non-shift Edge) between pairs of sub-images. Experiments
on two IQA databases (i.e. LIVE and CSIQ databases) show that by using only 6 features, the proposed metric can work
very well with high correlations between the subjective and objective scores. In particular, it works consistently well
across all the distortion types.
Bit allocation is a key issue in image/video coding. An optimal bit allocation can improve the encoding performance,
which means to maximize the image/video quality in the constraint of bit rate, or vice versa, to minimize the bit rate with
a restrictive quality. We suggest that the bit allocation for macro blocks (MBs) can be optimized by aiming at the
constant perceptual quality (CPQ) inside an image/a frame. Based on the MINMAX criterion, we proposed a multi-pass
block-layer bit allocation scheme for intra frame encoding, in which all the local areas in a frame get approximately the
same perceptual quality by choosing the quantization parameter (QP) for each MB. The experimental results show that
the proposed method can improve the encoding performance obviously.
Single sensor digital color cameras capture only one of the three primary colors at each pixel and a process called color demosaicking (CDM) is used to reconstruct the full color images. Most CDM algorithms assume the existence of high local spectral redundancy in estimating the missing color samples. However, for images with sharp color transitions and high color saturation, such an assumption may be invalid and visually unpleasant CDM errors will occur. In this paper, we exploit the image nonlocal redundancy to improve the local color reproduction result. First, multiple local directional estimates of a missing color sample are computed and fused according to local gradients. Then, nonlocal pixels similar to the estimated pixel are searched to enhance the local estimate. An adaptive thresholding method rather than the commonly used nonlocal means filtering is proposed to improve the local estimate. This allows the final reconstruction to be performed at the structural level as opposed to the pixel level. Experimental results demonstrate that the proposed local directional interpolation and nonlocal adaptive thresholding method outperforms many state-of-the-art CDM methods in reconstructing the edges and reducing color interpolation artifacts, leading to higher visual quality of reproduced color images.
Dual-energy digital mammography (DEDM) can suppress the contrast between adipose and glandular tissues and
generate dual-energy (DE) calcification signals. DE calcification signals are always influenced by many factors. Image
noise is one of these factors. In this paper, the sensitivity of DE calcification signal to image noise was analyzed based
on DEDM physical model. Image noise levels of two different commercially available digital mammography systems,
GE Senographe Essential system and GE Senographe DS system, were measured. The mean noise was about 1.04% for
Senographe Essential system, 1.42% for Senographe DS system at 28kVp/50mAs; and was 0.47% for Senographe
Essential system, 0.79% for Senographe DS system at 48kVp/12.5mAs. Evaluations were performed by comparing RMS
(Root-Mean-Square) of calcification signal fluctuations in background regions and CNR (Contrast-Noise-Ratio) of
calcification signals in clusters when these two digital mammography systems were used. The results showed that image
noise had a serious impact on DEDM calcification signals. If GE Senographe Essential system was used, calcification
signal fluctuations were 200~300μm, and when calcification size is greater than 300μm, the probability of acquiring
CNR≥3 is over 50%. If noise reduction techniques are used, the calcification threshold size of CNR≥3 can be lower.
The research on image quality assessment (IQA) has been become a hot topic in most area concerning image processing.
Seeking for the efficient IQA model with the neurophysiology support is naturally the goal people put the efforts to
pursue. In this paper, we argue that comparing the edges position of reference and distorted image can well measure the
image structural distortion and become an efficient IQA metric, while the edge is detected from the primitive structures
of image convolving with LOG filters. The proposed metric is called NSER that has been designed following a simple
logic based on the cosine distance of the primitive structures and two accessible improvements. Validation is taken by
comparison of the well-known state-of-the-art IQA metrics: VIF, MS-SSIM, VSNR over the six IQA databases: LIVE,
TID2008, MICT, IVC, A57, and CSIQ. Experiments show that NSER works stably across all the six databases and
achieves the good performance.
We propose a practical context-based adaptive image resolution upconversion algorithm. The basic idea is to use a low-resolution (LR) image patch as a context in which the missing high-resolution (HR) pixels are estimated. The context is quantized into classes and for each class an adaptive linear filter is designed using a training set. The training set incorporates the prior knowledge of the point spread function, edges, textures, smooth shades, etc. into the upconversion filter design. For low complexity, two 1-D context-based adaptive interpolators are used to generate the estimates of the missing pixels in two perpendicular directions. The two directional estimates are fused by linear minimum mean-squares weighting to obtain a more robust estimate. Upon the recovery of the missing HR pixels, an efficient spatial deconvolution is proposed to deblur the observed LR image. Also, an iterative upconversion step is performed to further improve the upconverted image. Experimental results show that the proposed context-based adaptive resolution upconverter performs better than the existing methods in both peak SNR and visual quality.
X-ray scatter leads to erroneous calculations of dual-energy digital mammography (DEDM). The existing methods for
scatter correction in DEDM are using anti-scatter grids or the pinhole-array interpolation method which is complicated
and impractical. In this paper, a scatter correction algorithm for DEDM is developed based on the knowledge that scatter
radiation in mammograms varies slowly and most pixels in mammograms are non-microcalcification pixels. The
proposed algorithm only uses the information of low-energy (LE) and high-energy (HE) images. And it doesn't need
anti-scatter grids, lead sheet and extra exposures. Our results show that the proposed scatter correction algorithm is
effective. When using the simple least-squares fit and linear interpolation, the scatter to primary ratio (SPR) can be
decreased from ~33.4% to ~2.8% for LE image and from ~26.2% to ~0.8% for HE image. Applying scatter correction to
LE and HE images, the resultant background signal in the DE (dual-energy) calcification image can be reduced
Image demosaicing is a problem of interpolating full-resolution color images from so-called color-filter-array
(CFA) samples. Among various CFA patterns, Bayer pattern has been the most popular choice and demosaicing
of Bayer pattern has attracted renewed interest in recent years partially due to the increased availability of source
codes/executables in response to the principle of "reproducible research". In this article, we provide a systematic
survey of over seventy published works in this field since 1999 (complementary to previous reviews22, 67).
Our review attempts to address important issues to demosaicing and identify fundamental differences among
competing approaches. Our findings suggest most existing works belong to the class of sequential demosaicing
- i.e., luminance channel is interpolated first and then chrominance channels are reconstructed based on recovered
luminance information. We report our comparative study results with a collection of eleven competing
algorithms whose source codes or executables are provided by the authors. Our comparison is performed on
two data sets: Kodak PhotoCD (popular choice) and IMAX high-quality images (more challenging). While
most existing demosaicing algorithms achieve good performance on the Kodak data set, their performance on
the IMAX one (images with varying-hue and high-saturation edges) degrades significantly. Such observation
suggests the importance of properly addressing the issue of mismatch between assumed model and observation
data in demosaicing, which calls for further investigation on issues such as model validation, test data selection
and performance evaluation.
Spectropolarimetric imaging can provide useful discriminating information for human face recognition that cannot be
obtained by other imaging methods. This paper examines the ability of face recognition by using spectropolarimetric
images. The Spectropolarimetric images were collected by using a CCD camera equipped with a liquid crystal tunable
filter, which could capture 32 bands of images over the visible and near-infrared light (0.4μm-0.72μm). Since
polarization techniques have better contrast mechanisms for tissue imaging and spectroscopy, and can also provide
additional information about the structure of tissues, it is expected that better discriminate performance can be obtained
by using polarimetric and spectral information than just using spectral information. An algorithm for facial
characteristics analysis is presented to exploit only the spectropolarimetric information from different types of facial
tissues. Experiments demonstrate that the proposed algorithm can distinguish efficiently the different facial tissues.