Image denoising approach has been studied for many decades. The main focus of image denosing is how to preserve image detail while remove image noise, however, it is hard to precisely distinguish the detail from noise. Up to now, even the state-of-the-art methods have the disadvantage of smoothing the detail with the noise at the same time. Inspired by the guided image filter(GIF) approach, we come up with a brand new approach to eliminate the image noise without losing the detail. A high quality guidance image is reconstructed by the specific fusion method of the near infrared image and the RGB image. With the reconstructed guidance image, the GIF approach can provide precise guidance filtering effect on the noisy RGB image. This approach performs good under strong noise level without smoothing the detail. Theoretical analysis and experimental results demonstrate that our method performs much better than the proposed methods. Examples have given illustrations of the proposed method.
BEEPS(bi-exponential edge-preserving filter) is used to enhance the details of infrared image in this paper. The original infrared image has a dynamic range of 12 or 14 bits, and the human observation range is only 8 bits. Usually, the original infrared image needs to be compressed and displayed by gray-scale remapped for displaying. For example, automatic gain control and histogram equalization are the most widely used image display technologies in infrared imaging systems, but they can lead to the loss of local details, and it is difficult to control the visibility of weak details in images. Therefore, an infrared image digital detail enhancement algorithm has emerged. Current digital enhancement algorithms can effectively enhance image details and avoid over-amplification of noise, but there are still some drawbacks, such as large computational load and poor application flexibility. Therefore, we use BEEPS in our algorithm to overcome these problems. This algorithm uses a two dimensional convolution to separate the detail information from an original infrared image, and turn the original image into the detail layer and the base layer. Detail layer processing is to transform two-dimensional convolution into one-dimensional convolution, and to complete one-dimensional convolution through iterative calculation. Then, the enhanced detail layer is added back to the base frequency layer of histogram equalization. This not only improves the computational efficiency, but also improves the visual quality of the original image. The BEEPS algorithm is proved to be excellent by image and data testing.
We theoretically investigate the characteristics of the multilayered hyperbolic metamaterial (HMM) composed of graphene and discuss the transmission properties from another angle of Fabry–Perot (F–P) resonance analysis. Dispersion characteristics of graphene-dielectric multilayered hyperbolic metamaterials (GDM HMMs) can be adjusted by changing the chemical potential of graphene. Transfer matrix method is improved to adapt the condition of large tangential vectors, and transmission properties are analyzed numerically. Calculated results indicate that dielectric material and graphene codetermine the dispersion properties of the HMMs, and the optical properties can be dynamically adjusted due to the introduction of graphene. Transmission spectra exhibit F–P resonance properties and discussions prove the validity of the F–P cavity theory. However, the transmission characteristics of GDM HMMs are different from the phenomena and laws of the traditional F–P cavity. Further analysis reveals that the mechanism originates from the contribution of graphene and high-k waves in HMMs. We present an innovative perspective for investigating and understanding transmission properties of GDM HMMs and provide references for design of HMMs and other related photonic devices.
In this paper, we proposed a new infrared image detail enhancement approach. This approach could not only achieve the goal of enhancing the digital detail, but also make the processed image much closer to the real situation. Inspired by the joint-bilateral filter, two adjacent images were utilized to calculate the kernel functions in order to distinguish the detail information from the raw image. We also designed a new kernel function to modify the joint-bilateral filter and to eliminate the gradient reversal artifacts caused by the non-linear filtering. The new kernel is based on an adaptive emerge coefficient to realize the detail layer determination. The detail information was modified by the adaptive emerge coefficient along with two key parameters to realize the detail enhancement. Finally, we combined the processed detail layer with the base layer and rearrange the high dynamic image into monitor-suited low dynamic range to achieve better visual effect. Numerical calculation showed this new technology has the best value compare to the previous research in detail enhancement. Figures and data flowcharts were demonstrated in the paper.
During the reconstruction of a digital hologram, the reconstructed image is usually
degraded by speckle noise, which makes it hard to observe the original object pattern. In this
paper, a new reconstructed image enhancement method is proposed, which first reduces the
speckle noise using an adaptive Gaussian filter, then calculates the high frequencies that
belong to the object pattern based on a frequency extrapolation strategy. The proposed
frequency extrapolation first calculates the frequency spectrum of the Fourier-filtered image,
which is originally reconstructed from the +1 order of the hologram, and then gives the initial
parameters for an iterative solution. The analytic iteration is implemented by continuous
gradient threshold convergence to estimate the image level and vertical gradient information.
The predicted spectrum is acquired through the analytical iteration of the original spectrum
and gradient spectrum analysis. Finally, the reconstructed spectrum of the restoration image is
acquired from the synthetic correction of the original spectrum using the predicted gradient
spectrum. We conducted our experiment very close to the diffraction limit and used low
quality equipment to prove the feasibility of our method. Detailed analysis and figure
demonstrations are presented in the paper.
In this paper, we propose an interframe phase-correlated registration scene-based nonuniformity correction technology. This technology is based on calculating the correlated phase information between two neighboring frames to determine the precise overlapping area of them. Usually, the common registration algorithms use the scene motion information to calculate the relative displacement of neighboring frames to determine the overlapping area. This approach may be interfered by the level of nonuniformity and cause the registration error. Furthermore, bring negative consequences to the correction process. Our technology effectively conquers this worry, and makes the level of nonuniformity careless during the registration process. We also adopt a new gain coefficient convergent method which proposed by our lasted study to finish the correction. The whole technology works with great performance. Detailed analysis, images and flow charts of this technology are also provided.
In this paper, we propose a novel image detail enhancement technology which is well solved the problem of how to suppress the noise and enhance the detail at the same time of the infrared image. This technology is based on the layer separation idea. In nowadays, this idea is studied by many researchers, and many detail enhancement algorithms have been come up through this idea such as the bilateral filter for detail enhancement. According to our research, these algorithms although have the advantages of enhancing the detail without enhancing the noise, they also have the disadvantages of massive calculation, low speed and the worst is the gradient flipping effect which cause the enhanced image distorted. Our solution is based on the Guided Image Filter (GIF) to deal the separated detail layer of an image. The gradient flipping effect will be greatly suppressed with the priority that the GIF is a linear filter. Which means that the processed image will become much closer to the original image. We determine an adaptive weighting coefficient as the filter kernel. After that, we compress the base component into the display range by our modified histogram projection and enhance the detail component using the gain mask of the filter weighting coefficient. At last, we recombine the two parts and quantize the result to 8-bit domain. Experimental verification and detailed realization have been provided in this paper. We also have done significant comparison between our method and the proposed algorithm to show the superiority of our algorithm.
In this paper, we propose a new projection-based registration scene based non-uniformity correction technology. The hardware realization of this technology in the single FPGA cored real-time system has been detailed too. As we know, many kinds of scene based non-uniformity correction algorithm have been studied and achieved great successes. However, some of them are less effective, some are too complex to realize in hardware. These problems limit their application in engineering. We concentrate on the correction effect, the amount of computation and the hardware realizability, and finally raised this technology. It has the advantage of simple calculation, fast speed and accurate results. We have already applied this technology in the real system for some particular usage.
In this paper, we propose a new scene-based non-uniformity correction
algorithm based on FPGA. We focus on the real application and the non-uniformity
performance of the IR imager system and we did the research of the defects of other
non-uniformity correction algorithms which had already been reported nowdays and
found out that most of them can only be used in the lab. The scene set for these
algorithms often fits themselves in most cases. In real application, the scene is mostly
towards one continuous monotonous direction and has different appearance of
non-uniformity. We solve these problems by developing a new projection estimator
for the registration with a criterion, and immigrate this algorithm into a FPGA_based
hardware system. This system is fully engineered for some particular usage. We test
the performance of our technology by the evaluation indexes, and demonstrate the
actual effect of correcting the non-uniformity under a monotonous motion on the
system. At the end of this paper, we make a conclusion and perspective of our
research work.
Dynamic range reduction and detail enhancement are two important issues for effectively displaying high-dynamic-range images acquired by thermal camera systems. They must be performed in such a way that the high dynamic range image signal output from sensors is compressed in a pleasing manner for display on lower dynamic range monitors without reducing the perceptibility of small details. In this paper, a new method of display and detail enhancement for high dynamic range infrared images is presented. This method effectively maps the raw acquired infrared image to 8-bit domain based on the same architecture of bilateral filter and dynamic range partitioning approach. It includes three main steps: First, a bilateral filter is applied to separate the input image into the base component and detail component. Second, refine the base and detail layer using an adaptive Gaussian filter to avoid unwanted artifacts. Then the base layer is projected to the display range and the detail layer is enhanced using an adaptive gain control approach. Finally, the two parts are recombined and quantized to 8-bit domain. The strength of the proposed method lies in its ability to avoid unwanted artifacts and adaptability in different scenarios. Its great performance is validated by the experimental results tested with two real infrared imagers.
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