• Journal of Information Processing Systems
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• v.14 no.2
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• pp.552-562
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• 2018

With the advent of the information society, image restoration technology has aroused considerable interest. Guided image filtering is more effective in suppressing noise in homogeneous regions, but its edge-preserving property is poor. As such, the critical part of guided filtering lies in the selection of the guided image. The result of the Expected Patch Log Likelihood (EPLL) method maintains a good structure, but it is easy to produce the ladder effect in homogeneous areas. According to the complementarity of EPLL with guided filtering, we propose a method of coupling EPLL and guided filtering for image de-noising. The EPLL model is adopted to construct the guided image for the guided filtering, which can provide better structural information for the guided filtering. Meanwhile, with the secondary smoothing of guided image filtering in image homogenization areas, we can improve the noise suppression effect in those areas while reducing the ladder effect brought about by the EPLL. The experimental results show that it not only retains the excellent performance of EPLL, but also produces better visual effects and a higher peak signal-to-noise ratio by adopting the proposed method.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.85-96
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• 2020

It is important to extract salient object image and to solve the invariance problem for image recognition. In this paper we propose a new hybrid algorithm for invariance and improved classification performance in image recognition, whose algorithm is combined by FT(Frequency-tuned Salient Region Detection) algorithm, Guided filter, Zernike moments, and a simple artificial neural network (Multi-layer Perceptron). The conventional FT algorithm is used to extract initial salient object image, the guided filtering to preserve edge details, Zernike moments to solve invariance problem, and a classification to recognize the extracted image. For guided filtering, guided filter is used, and Multi-layer Perceptron which is a simple artificial neural networks is introduced for classification. Experimental results show that this algorithm can achieve a superior performance in the process of extracting salient object image and invariant moment feature. And the results show that the algorithm can also classifies the extracted object image with improved recognition rate.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.27-33
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• 2018

In this paper, we propose an efficient edge preserving smoothing filter for Infrared image that can reduce noise while preserving edge information. Infrared images suffer from low signal-to-noise ratio, low edge detail information and low contrast. So, detail enhancement and noise reduction play crucial roles in infrared image processing. We first apply a guided image filter as a local analysis. After the filtering process, we optimization globally using relativity of guided image filter. Our method outperforms the previous methods in removing the noise while preserving edge information and detail enhancement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3092-3107
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• 2019

Visible-infrared image fusion is a process of synthesizing an infrared image and a visible image into a fused image. This process synthesizes the complementary advantages of both images. The infrared image is able to capture a target object in dark or foggy environments. However, the utility of the infrared image is hindered by the blurry appearance of objects. On the other hand, the visible image clearly shows an object under normal lighting conditions, but it is not ideal in dark or foggy environments. In this paper, we propose a multi-guided filter and a real-time image fusion method. The proposed multi-guided filter is a modification of the guided filter for multiple guidance images. Using this filter, we propose a real-time image fusion method. The speed of the proposed fusion method is much faster than that of conventional image fusion methods. In an experiment, we compare the proposed method and the conventional methods in terms of quantity, quality, fusing speed, and flickering artifacts. The proposed method synthesizes 57.93 frames per second for an image size of $320{\times}270$. Based on our experiments, we confirmed that the proposed method is able to perform real-time processing. In addition, the proposed method synthesizes flicker-free video.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.23-30
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• 2016

In this paper, we propose an adaptive histogram projection technique for dynamic range compression and an efficient detail enhancement method which is enhancing strong edge while reducing noise. First, The high dynamic range image is divided into low-pass component and high-pass component by applying 'guided image filtering'. After applying 'guided filter' to high dynamic range image, second, the low-pass component of the image is compressed into 8-bit with the adaptive histogram projection technique which is using global standard deviation value of whole image. Third, the high-pass component of the image adaptively reduces noise and intensifies the strong edges using standard deviation value in local path of the guided filter. Lastly, the monitor display image is summed up with the compressed low-pass component and the edge-intensified high-pass component. At the end of this paper, the experimental result show that the suggested technique can be applied properly to the IR images of various scenes.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.417-426
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• 2023

To improve the effect of image restoration and solve the image detail loss, an image dehazing enhancement algorithm based on mean guided filtering is proposed. The superpixel calculation method is used to pre-segment the original foggy image to obtain different sub-regions. The Ncut algorithm is used to segment the original image, and it outputs the segmented image until there is no more region merging in the image. By means of the mean-guided filtering method, the minimum value is selected as the value of the current pixel point in the local small block of the dark image, and the dark primary color image is obtained, and its transmittance is calculated to obtain the image edge detection result. According to the prior law of dark channel, a classic image dehazing enhancement model is established, and the model is combined with a median filter with low computational complexity to denoise the image in real time and maintain the jump of the mutation area to achieve image dehazing enhancement. The experimental results show that the image dehazing and enhancement effect of the proposed algorithm has obvious advantages, can retain a large amount of image detail information, and the values of information entropy, peak signal-to-noise ratio, and structural similarity are high. The research innovatively combines a variety of methods to achieve image dehazing and improve the quality effect. Through segmentation, filtering, denoising and other operations, the image quality is effectively improved, which provides an important reference for the improvement of image processing technology.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.339-346
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• 2003

MRI provides anatomical structure information with superb spatial resolution that can be utilized in clinical surgeries. Advanced image processing techniques in conjunction with the MRI-guided surgery is expected to be of great importance in brain surgeries in the near future. In this paper, we introduce an image-guided surgery technique using the stereo matching method. To perform image-guided biopsy operations, we made MRI markers, camera markers and a detection probe marker. To evaluate the accuracy of the image-guided system. we made a silicone phantom. Using the phantom and markers, we have performed MRI-guided experiments with a 1.5 Tesla MRI system. It has been verified from phantom experiments that our system has a positioning error less than 1.5%. Compared with other image guided surgery system, our system shows better positioning accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1507-1514
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• 2016

Full-image guided filter reflects all pixels of image in filtering by using weight propagation and two-pass model, whereas the existing guide filter is processed based on the kernel window. Therefore the computational complexity can be improved while maintaining characteristics of guide filter, such as edge-preserving, smoothing, and so on. In this paper, we propose an efficient VLSI architecture for the full-image guided filter by analyzing the data dependency, the data frequency and the PSNR analysis of the image in order to achieve enough speed for various applications such as stereo vision, real-time systems, etc. In addition, the proposed efficient scheduling enables the realtime process by minimizing the idle period in weight computation. The proposed VLSI architecture shows 214MHz of maximum operating frequency (image size: 384*288, 965 fps) and 76K of gates (internal memory excluded).

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.283-292
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• 2018

Devices capable of capturing and displaying high dynamic range (HDR) images with significantly increased brightness range compared to low dynamic range (LDR) images have been developed and various methods for efficiently converting the brightness range of an image have been developed. In this paper, we propose a reverse tone mapping method using a guided filter to efficiently convert LDR images into HDR images. After obtaining brightness enhancement function (BEF) using a guided filter, we can reconstruct HDR image from one LDR image. In addition, when the image is too bright or dark, the proposed method maximizes the image quality of the reconstructed HDR image by estimating and adjusting the exposure value before expanding the brightness range of images. Computer simulations show that the proposed method produces HDR images of superior quality compared with the conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.327-346
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• 2019

The goal of hyperspectral pansharpening is to combine a hyperspectral image (HSI) with a panchromatic image (PANI) derived from the same scene to obtain a single fused image. In this paper, a new hyperspectral pansharpening approach using adaptive weighted regression and guided filter is proposed. First, the intensity information (INT) of the HSI is obtained by the adaptive weighted regression algorithm. Especially, the optimization formula is solved to obtain the closed solution to reduce the calculation amount. Then, the proposed method proposes a new way to obtain the sufficient spatial information from the PANI and INT by guided filtering. Finally, the fused HSI is obtained by adding the extracted spatial information to the interpolated HSI. Experimental results demonstrate that the proposed approach achieves better property in preserving the spectral information as well as enhancing the spatial detail compared with other excellent approaches in visual interpretation and objective fusion metrics.