• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.118-121
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• 2014

Image interpolation is a method of determining the value of new pixel coordinate in the process of image scaling. Recently, image contents are likely to be a large-capacity, interpolation algorithm is required to generate fast enhanced result image. In this paper, fast multiple mixed image interpolation for image resolution enhancement is proposed. The proposed method estimates expected 12 shortfalls from four sub-images of a input image, and generates the result image that is interpolated in the combination of the expected shortfalls with the input image. The experimental results demonstrate that PSNR increases maximum value of 1.9dB, SSIM increases maximum value of 0.052, and the subjective quality is superior to any other compared methods. Moreover, it is known by algorithm running time comparison that the proposed method has been at least three times faster than the compared conventional methods. The proposed method can be useful for application on image resolution enhancement.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.18-26
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• 2009

Image resolution enhancement algorithm is a basic technique for image enlargement and restoration. The main problem is the image quality degradation such as blurring or blocking effects. In this paper, we propose loss information estimation and image resolution enhancement method using low level interpolation method. In the proposed method, loss information is computed by downsampling -interpolation process of obtained low resolution image. We estimate loss information of high resolution image using interpolation of the computed loss information. Lastly, we add up interpolated high resolution image and the estimated loss information which is applied a weight factor. Our experiments obtained the average PSNR 1.4dB which is improved results better than conventional algorithm. Also subjective image quality is more clearness and distinctness. The proposed method may be helpful for various video applications which required improvement of image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.39-44
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• 2004

The three-dimensional (3D) wavelet transform with motion compensation is suitable for very high quality video coding due to both spatial and temporal decorrelations. However, it still suffers from image degradation such as ringing artifact and afterimage because of the loss of high frequency components by quantization. This paper proposes an iterative regularized enhancement of the motion-compensated 3D wavelet coded video. We also propose the adaptive implementation of the constraints for the regularization. It selectively suppresses the high frequency component along only the corresponding edge direction.

• Journal of Broadcast Engineering
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• v.2 no.2
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• pp.169-181
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• 1997

In this paper, we propose and discuss about image enhancement techniques for MPEG-4. which represents very low bit-rate, content-based. and object-based hierarchical audio-visual coding standard. The proposed enhancement technique removes undesired artifacts arising in the compression procedure and increase resolution in both spatial and temporal domains. In order to remove undesired artifacts. we divide the MPEG-4 video algorithm in two parts: MPEG-2 like part and the new part. For removing artifacts caused by the first part. we adopt the conventional blocking artifacts algorithm developed for MPEG-2. On the other hand for removing artifacts caused by the second part. we provide a new degradation model. and propose the corresponding image restoration method. For increasing resolution of the MPEG-4 images, we propose a general framework of multichannel image interpolation process. which includes both spatial and temporal interpolations. As the MPEG-4 standard is under development. various sophisticated techniques are considered. but research on image enhancement techniques is relatively underestimated. By this reason. additional image enhancement techniques will become very important issue in realization phase of MPEG-4.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.405-410
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• 2010

Image processing operations like smoothing and edge detection, and many more are very widely used in areas like Computer Vision. We classify the image processing domain as seven branches-image acquirement and output, image coding and compression, image enhancement and restoration, image transformation, image segmentation, image description, and image recognition and description. We implemented algorithms of gaussian smoothing, laplace sharpening, image contrast effect, image black and white effect, image fog effect, image bright and dark effect, image median filter, and canny edge detection. Such experimental results show the figures respectively.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.115-118
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• 2004

Images with high resolution are desired and often required in many visual applications. When resolution can not be improved by replacing sensors, either because of cost or hardware physical limits, super resolution image reconstruction method is what can be resorted to. Super resolution image reconstruction method refers to image processing algorithms that produce high quality and high resolution images from a set of low quality and low resolution images. The method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. The method can be either the frequency domain approach or the spatial domain approach. Much of the earlier works concentrated on the frequency domain formulation, but as more general degradation models were considered, later researches had been almost exclusively on spatial domain formulations. The method in spatial domains has three stages: i) motion estimate or image registration, ii) interpolation onto high resolution grid and iii) deblurring process. The super resolution grid construction in the second stage was discussed in this paper. We applied the Maximum A­Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from a set of low resolution images and compared the results with those from other known interpolation methods.

• Imaging Science in Dentistry
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• v.50 no.4
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• pp.331-337
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• 2020

Purpose: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. Materials and Methods: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. Results: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. Conclusion: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.

• International Journal of Advanced Culture Technology
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• v.4 no.4
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• pp.75-80
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• 2016

The process of extracting external boundary of an object is a very important process for recognizing an object in the image. The proposed extraction method consists of two processes: External Boundary Extraction and Thinning. In the first step, external boundary extraction process separates the region representing the object in the input image. Then, only the pixels adjacent to the background are selected among the pixels constituting the object to construct an outline of the object. The second step, thinning process, simplifies the outline of an object by eliminating unnecessary pixels by examining positions and interconnection relations between the pixels constituting the outline of the object obtained in the previous extraction process. As a result, the simplified external boundary of object results in a higher recognition rate in the next step, the object recognition process.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.237-240
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• 2020

The fine-tuned neural network (NN) model for a whole temporal portion in a video does not always yield the best quality (e.g., PSNR) performance over all regions of each frame in the temporal period. For certain regions (usually homogeneous regions) in a frame for super-resolution (SR), even a simple bicubic interpolation method may yield better PSNR performance than the fine-tuned NN model. When there are multiple NN models available at the receivers where each NN model is trained for a group of images having a specific category of image characteristics, the performance of Quality enhancement can be improved by selectively applying an appropriate NN model for each image region according to its image characteristic category to which the NN model was dedicatedly trained. In this case, it is necessary to signal which NN model is applied for each region. This is very advantageous for image restoration and quality enhancement (IRQE) applications at user terminals with limited computing capabilities.

• Journal of Broadcast Engineering
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• v.1 no.2
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• pp.142-151
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• 1996

The digital video cassette recorder(DVCR) is considered as next generation VCR due to its performance breakthrough in various aspects, such as digital recording and various digital image processing techniques. The purpose of our study is to understand the standardized specifications of the DVCR system, evaluate the performance of the system, and improve the quality of the reconstructed DVCR image. More specifically, in order to enhance the DVCR Image we consider a series of discrete cosine transform(DCT), quantization, inverse DCT, and Inverse quantization as a degradation process of the Imaging system, and propose a fast adaptive image restoration algorithm for reducing blocking artifacts.