Earticle

If a image doesn't include sufficient data of output size and resolution, we will scan again the image. Interpolation generates a new pixel by methematical average of processing. In the interpolation method, there are nearest neighbor interpolation, bilinear interpolation and bicubic interpolation etc. This study was carried out for the purpose of researching compatible method to digital scan image caused by only different interpolation methods. Nearest neighbor interpolation show superior effect in the drawing image. Bilinear interpolation show reduction in detail and contrast. Bicubic interpolation show superior effect in the digital photo image USM(Unsharp Mask) application after extension by interpolation show better than extension by interpolation after USM(unsharp mask) application.

본 논문은 단일 이미지에서 초해상도 영상 생성을 위해 저해상도 이미지에서 생성한 패치정보를 기반으로 선형 보간하는 방법을 제안하였다. 기존의 초해상도 생성 방법인 전역 공간의 회귀 모델을 사용하면 특정 영역에 대해 참조할 정보가 부족하여 일반적으로 품질이 떨어지는 결과가 나타난다. 이러한 결과를 보완하기 위해 제안하는 방법은 초해상도 이미지 생성 과정에서 영상을 패치 단위로 지역을 분할하여 의미있는 정보를 수집하고, 수집된 정보를 기반으로 초해상도 이미지 생성을 위해 확장시킨 이미지 매트릭스 영역의 구성정보를 분석하여 선형 보간 과정을 거치고 패치정보를 대응시켜 탐색한 최적의 패치 정보를 기준으로 선형 보간하는 방법을 제안하였다. 실험을 위해 원본 이미지를 복원된 영상과 PSNR, SSIM으로 비교 평가하였다.

In this paper, we propose a linear interpolation method based on patch information generated from a low - resolution image for generating a super resolution image in a single image. Using the regression model of the global space, which is a conventional super resolution generation method, results in poor quality in general because of lack of information to be referred to a specific region. In order to compensate for these results, we propose a method to extract meaningful information by dividing the region into patches in the process of super resolution image generation, analyze the constituents of the image matrix region extended for super resolution image generation, We propose a method of linear interpolation based on optimal patch information that is searched by correlating patch information based on the information gathered before the interpolation process. For the experiment, the original image was compared with the reconstructed image with PSNR and SSIM.

Super Resolution (SR) reconstruction is a procedure of attaining the High resolution (HR) images from a set of noisy and blurred low resolution (LR) observations. In this paper a novel SR algorithm is proposed by developing a fuzzy based Bicubic interpolation algorithm to improve the resolution of the image. The algorithm significantly improves the resolution, quality and enhances the color information of the reconstructed image when compared with the other existing methods.

In order to decrease the smoothing effect of traditional linear interpolation methods and avoid the complexity of edge-directed adaptive algorithms, a novel image interpolation algorithm based on the directional extension of linear interpolation kernel is proposed. At first, All Phase Non-subsampled Contourlet Transform is used to divide the image of low resolution into different frequencies and directional sub-band images, and then the All Phase Discrete Cosine Transform Interpolation Kernel is extended to 2-D with distinct directional priorities and used to enlarge respective sub-band images. The interpolated sub-band images are finally synthesized as the upsampled image. Simulation results demonstrate that the proposed algorithm improves both the subjective and objective quality of the interpolated images over conventional linear interpolation. The computation complexity of the proposed algorithm is moderate and it can be implemented with parallel structure so as to save more computation time.

In this paper, a new wavelet based image interpolation algorithm is proposed for remote sensing images. Remote sensing images include many mid and high frequency elements such as aerial and satellite photograph images. These frequency elements contain important information. Because conventional interpolation techniques have blurring or blocking effects, the quality of interpolated remote sensing images is not good over that of standard images like lenna. The proposed interpolation technique uses bicubic interpolation and DWT to address these problems. Experimental result shows that the proposed technique outperforms the conventional techniques in terms of PSNR.

In this paper, we propose an image interpolation algorithm based on the characteristics of the Human Visual System (HVS). Firstly, the saliency map is extracted for the input image by the feature contrast. Based on the saliency detection algorithm, the input image can be divided into salient regions and non-salient regions. A non-local means based interpolation and the bicubic interpolation operations are adopted to implement the image interpolation for salient and non-salient regions in visual scenes, respectively. Experimental results show that the proposed method improves the perceptual quality of the salient regions during image interpolation. The PSNR and SSIM results demonstrate that the proposed method can obtain better performance than existing related image interpolation methods.

To reduce cost, current digital cameras adopt color filter array (CFA) instead of RGB camera which obtains beam splitters to capture image information. This leads effect that only one of three color channels remains and the other two color channels are missing during CFA. To compensate missing color information, color interpolation or demosaicking process is needed. There have been many methods with different degree of complexity and filter size. Normally bigger filters and higher complexity provide better performance. However, clever method may give fast and satisfactory results in terms of objective and subjective performance. This paper proposes adaptive camera image interpolation method which uses directional information. Simulation results show that the proposed method outperforms conventional benchmark method.

In this paper, a new wavelet based image interpolation algorithm using pixel-by-pixel (PBP) cubic image interpolation and hybrid filter is proposed for images which include many mid and high frequency elements. There is much important information in frequency bands. Because conventional interpolation techniques have blurring or blocking effects, the quality of interpolated remote sensing images is not good over that of standard test images. The proposed interpolation technique uses PBP, DWT and hybrid filter to address these problems preserving thin lines and edges information. Simulation result shows that the proposed technique outperforms the conventional techniques in terms of PSNR.

In this paper, we use Newton’s polynomial for image upsampling, especially quincunx arrayed image. The Newton interpolation is entitled after its inventor Isaac Newton, which is polynomial for a provided set of data points. This formula is important because Newton interpolation is a simplified version of Taylor interpolation. The only difference is Newton polynomial used finite differences. Simulation results are provided. Three conventional methods such as nearest neighbor, bilinear and bicubic are compared with the proposed method. It can be concluded that the proposed method yields the best performance.

The color filter array (CFA) captures only one-third of the necessary color intensities and the full color image is generated from the captured data by interpolation. In recent years, the algorithm of Bayer patterned image compression based on “structure separation” has achieved better image quality. On the basis of previous work, the algorithm based on the all phase biorthogonal transform (APBT) and interpolation is proposed in this paper. Instead of the conventional DCT-JPEG, the APBT-JPEG significantly reduces complex multiplications and makes the quantization table easier. Several kinds of interpolation methods to the decompressed image data are also discussed in this paper, including nearest neighbor interpolation, bilinear interpolation, cubic convolution interpolation and a novel interpolation method based on APIDCT. Experimental results show that the proposed algorithm outperforms the one based on “structure separation”; and the APIDCT interpolation performs close to the conventional interpolation methods and behaves better than them at high bit rates.

In this paper, a novel image zooming scheme for indexed color images is proposed. In the proposed scheme, the bilinear interpolation technique is employed to enlarge the original image. Then, the pixel grouping process is executed to classify the pixels in the enlarged image. From the experimental results, it is shown that the proposed scheme provides better image quality than the pixel copy technique for the zooming of the indexed color images.

To improve the accuracy of digital image edge detection, an ENO nonlinear fourth-order interpolation based subpixel edge detection algorithm was proposed in this paper. A stencil was constructed through classical Canny operator, followed by processing gray images to generate gradient images. ENO nonlinear fourth-order interpolation was applied in the gradient direction of target edges, and then subpixel subdivision computation was performed to obtain subpixel accurate locations of target edges, the performance of our edge detection algorithm was compared with that of centered cubic convolution and non-centered cubic convolution based edge detection methods. Simulation results demonstrated that the ENO based interpolation detection algorithm improved the abilities of detecting image edges.

This article provides a comparison between methods of images amplification with good results in the preservation of the structural similarity of the output image. This parameter is evaluated through the method SSIM (structural similarity index).One of the methods used for amplification is the combination of SWT (stationary wavelet transform) for the decomposition of the image, SVD (singular value decomposition) for conservation of high frequency content and DWT ( discrete Wavelet Transform ) for obtain the high resolution image. This method has report of good results [5]. Other amplification technique is proposed, RHPI (pulses interpolation constrained by high frequency)[9].The procedure RHPI uses a theoretical model of digital image registration and high frequency filters for the construction of the interpolation kernel for the amplification process. Finally, both methods are compared through SSIM structural similarity coefficient and PSNR parameters. The proposed method RHPI is a solution better than the SWT-SVD-DWT for the improvement in the conservation of SSIM and PSNR ratios for high rates of amplification.

영상 확대 기술을 담당하는 업스케일러를 설계 할 때 보간 기법의 선택은 매우 중요하다. 적용하는 보간 기법에 따라 출력되는 영상의 화질과 스케일러 내부의 연산량 및 하드웨어 복잡도가 결정되기 때문이다. 디지털 영상 처리 기술의 발달과 함께 성능이 좋은 여러 가지 보간 기법들이 개발되어 오고 있지만 알고리즘의 복잡도를 고려하지 않은 기법들은 실시간 처리와 하드웨어 적용에 부적합한 경우가 많다. 비교적 좋은 성능을 보여주는 Cubic interpolation 역시 인접 화소 보간 기법이나 선형 보간 기법과 비교하면 훨씬 더 많은 연산량을 요구한다. 이런 높은 연산량의 대부분은 픽셀의 밝기값에 곱해지는 계수를 구하기 위한 복잡한 계산에서 기인한다. 따라서 본 논문에서는 cubic interpolation 의 복잡도를 낮춰 하드웨어 적용에 적합하도록 하기 위하여 LUT(Look-up Table)을 이용하는 방법을 제안하고 실험을 통해 그 결과를 보인다.

영상 확대 기술을 담당하는 업스케일러를 설계 할 때 보간 기법의 선택은 매우 중요하다. 적용하는 보간 기법에 따라 출력되는 영상의 화질과 스케일러 내부의 연산량 및 하드웨어 복잡도가 결정되기 때문이다. 디지털 영상 처리 기술의 발달과 함께 성능이 좋은 여러 가지 보간 기법들이 개발되어 오고 있지만 알고리즘의 복잡도를 고려하지 않은 기법들은 실시간 처리와 하드웨어 적용에 부적합한 경우가 많다. 비교적 좋은 성능을 보여주는 Cubic interpolation 역시 인접 화소 보간 기법이나 선형 보간 기법과 비교하면 훨씬 더 많은 연산량을 요구한다. 이런 높은 연산량의 대부분은 픽셀의 밝기값에 곱해지는 계수를 구하기 위한 복잡한 계산에서 기인한다. 따라서 본 논문에서는 cubic interpolation 의 복잡도를 낮춰 하드웨어 적용에 적합하도록 하기 위하여 LUT(Look-up Table)을 이용하는 방법을 제안하고 실험을 통해 그 결과를 보인다.

In this paper, we present an approach for matching images from finding interesting points and applying new image interpolation algorithm. New algorithms are developed that automatically align the input images match them and reconstruct 3-D surfaces. The interpolation algorithm is designed to cope with simple shapes. The proposed image interpolation algorithm generate a rotation image about vertical axes by an any angle from 4 base images. Each base image that was obtained from CCD camera has an angle difference of 90$^{\circ}$ The proposed image interpolation algorithm use the geometric analysis of image and depth information.

Surveillance Cameras such as CCTV easily found in places requiring security and the prevention of crimes such as public institutions, banks, etc. play an important role as they prevent all sorts of crimes, and provide a decisive clue fix settling a criminal case. But, in case that a far-off person is photographed, an original image should be enlarged to identify the person. And as for the technique of enlarging an image, it is important to enlarge and restore it close to its original image rather than to merely magnify it. For the enlargement and restoration of an image, techniques called interpolation are used; as for interpolation methods known hitherto, however, the higher the magnifying power is, the more deteriorated the quality of an image becomes to the extent that the image cannot be identified. Therefore, in this paper, we are going to propose a new technique whereby the face outline in an image is vectorized and restored by means of FDP(Facial Definition Parameter) standardized by the MPEG-4 SNHC FBA group, and an image is restored to have better quality than images restored with the existing interpolation.

In this paper, we present a data-adaptive least-squares method for interpolating noisy magnetic resonance (MR) images. The proposed method generates a denoised high-resolution output from a low-resolution MR image contaminated by additive Gaussian noise. To achieve both noise suppression and the preservation of important image features, such as edges and fine structures, we first construct least-squares approximation operators that incorporate penalties based on task-specific distance metrics, and then adaptively combine these operators according to the characteristics of image regions. Our algorithm can estimate unknown values at arbitrary query points, making it particularly useful for complex restoration tasks in medical imaging. The effectiveness of the proposed method in maintaining image quality while reducing noise and artifacts is demonstrated through experiments on simulated brain MR images.