Adaptive rational fractal interpolation function for image super-resolution via local fractal analysis

© 2019 Elsevier B.V. Image super-resolution aims to generate high-resolution image based on the given low-resolution image and to recover the details of the image. The common approaches include reconstruction-based methods and interpolation-based methods. However, these existing methods show difficulty in processing the regions of an image with complicated texture. To tackle such problems, fractal geometry is applied on image super-resolution, which demonstrates its advantages when describing the complicated details in an image. The common fractal-based method regards the whole image as a single fractal set. That is, it does not distinguish the complexity difference of texture across all regions of an image regardless of smooth regions or texture rich regions. Due to such strong presumption, it causes artificial errors while recovering smooth area and texture blurring at the regions with rich texture. In this paper, the proposed method produces rational fractal interpolation model with various setting at different regions to adapt to the local texture complexity. In order to facilitate such mechanism, the proposed method is able to segment the image region according to its complexity which is determined by its local fractal dimension. Thus, the image super-resolution process is cast to an optimization problem where local fractal dimension in each region is further optimized until the optimization convergence is reached. During the optimization (i.e. super-resolution), the overall image complexity (determined by local fractal dimension) is maintained. Compared with state-of-the-art method, the proposed method shows promising performance according to qualitative evaluation and quantitative evaluation

A Review of Image Super Resolution using Deep Learning

The image processing methods collectively known as super-resolution have proven useful in creating high-quality images from a group of low-resolution photographic images. Single image super resolution (SISR) has been applied in a variety of fields. The paper offers an in-depth analysis of a few current picture super resolution works created in various domains. In order to comprehend the most current developments in the development of Image super resolution systems, these recent publications have been examined with particular emphasis paid to the domain for which these systems have been designed, image enhancement used or not, among other factors. To improve the accuracy of the image super resolution, a different deep learning techniques might be explored. In fact, greater research into the image super resolution in medical imaging is possible to improve the data's suitability for future analysis. In light of this, it can be said that there is a lot of scope for research in the field of medical imaging

Image Super-Resolution Based on Fractal Analysis

University of Technology Sydney. Faculty of Engineering and Information Technology.Image super-resolution is an important problem in the computer vision field. Image super-resolution aims to generate high-resolution images with an ‘ideal’ appearance from low-resolution ones. From traditional interpolation methods (bilinear, bicubic et al.) to CNN methods, the quality of reconstructed HR image is highly improved. However, most of these methods are failing to keep texture details and edge structure, especially in highly complicated texture area. To tackle such problems, fractal geometry is applied to image super-resolution, which demonstrates its advantages when describing the complicated details in an image. The common fractal-based method does not distinguish the complexity difference of texture across all regions of image regardless of smooth regions or texture-rich regions. Due to such strong presumption, it causes artificial errors while recovering smooth area and texture blurring at the regions with rich texture. This thesis firstly proposes a rational fractal interpolation model with various setting in different regions to adapt to the local texture complexity. Secondly, it should keep the degree of image roughness non-decreasing, which reflects various texture features and appearance during the image super-resolution process. However, this point is not well addressed in the current work. This thesis argues that reducing roughness during image super-resolution is the key reason causing various problems such as artificial texture and/or edge blur. Here, keeping the image roughness non-decreasing during super-resolution is being well investigated for the first time to our best knowledge. Thirdly, fine details are more related to the information in the high-frequency spectrum on the Fourier domain. Most of the existing methods do not have specific modules to handle such high-frequency information adaptively. Thus, they cause edge blur or texture disorder. To tackle the problems, this thesis explores image super-resolution on multiple sub-bands of the corresponding image, which are generated by NonSubsampled Contourlet Transform (NSCT). Different sub-bands hold the information of different frequency which is then related to the detailedness of information of the given low-resolution image. Our extensive experimental results demonstrate that the proposed method achieves encouraging performance with state-of-the-art super-resolution algorithms

Content adaptive single image interpolation based super resolution of compressed images

Image Super resolution is used to upscale the low resolution Images. It is also known as image upscaling .This paper focuses on upscaling of compressed images based on Interpolation techniques. A content adaptive interpolation method of image upscaling has been proposed. This interpolation based scheme is useful for single image based Super-resolution (SR) methods .The presented method works on horizontal, vertical and diagonal directions of an image separately and it is adaptive to the local content of an image. This method relies only on single image and uses the content of the original image only; therefore the proposed method is more practical and realistic. The simulation results have been compared to other standard methods with the help of various performance matrices like PSNR, MSE, MSSIM etc. which indicates the preeminence of the proposed method

การปรับปรุงคุณภาพสำหรับภาพจากกล้องวงจรปิดโดยใช้เทคนิค (BPE BICUBIC-BASED PIXEL ESTIMATION TECHNIQUE FOR IMAGE ENHANCEMENT ON CLOSE CIRCUIT TELEVISION)

กล้องวงจรปิดเป็นอุปกรณ์ที่ใช้บันทึกภาพเคลื่อนไหวที่เกิดขึ้นได้ตลอดเวลา และบันทึกเหตุการณ์ต่าง ๆ ได้ดี แต่คุณภาพของภาพที่ได้จากการบันทึกจะขึ้นอยู่กับประสิทธิภาพและคุณสมบัติของระบบกล้องวงจรปิดและสภาพแวดล้อมต่าง ๆ ในการบันทึกภาพ กล้องวงจรปิดที่มีคุณภาพที่ดีจะมีราคาที่สูงกว่ากล้องวงจรปิดที่มีราคาต่ำทำให้ต้นทุนในการติดตั้งสูงตามไปด้วย งานวิจัยนี้ได้นำเสนอเทคนิคการปรับปรุงคุณภาพสำหรับภาพจากกล้องวงจรปิดที่มีคุณภาพต่ำ ให้ภาพที่ได้มีคุณภาพเพิ่มมากขึ้นในด้านความละเอียดและความคมชัดของภาพ โดยใช้เทคนิคการประมาณค่าพิกเซลที่ได้มีการพัฒนาขึ้น ประมวลผลร่วมกับเทคนิค Bicubic และภาพที่ใช้ในการทดลองเป็นภาพนิ่งที่ขยายภาพโดยใช้ค่าพีเอสเอ็นอาร์และเอสเอสไอเอ็ม วัดประสิทธิภาพเทคนิค จากผลการทดลองเทคนิคงานวิจัยกับภาพคุณภาพต่ำจากกล้องวงจรปิด มีค่า PSNR เฉลี่ยสูงสุด 19.341 และ SSIM เฉลี่ยสูงสุด 0.179 ซึ่งมีค่าสูงกว่าเทคนิคอื่นที่ใช้ในการทดลอง แสดงให้เห็นถึงความสามารถในการปรับปรุงคุณภาพของภาพด้วยวิธีประมาณค่าให้มีความละเอียดและความคมชัดมากขึ้นได้อย่างมีประสิทธิภาพคำสำคัญ: การปรับปรุงคุณภาพ  การประมาณค่า  ระบบกล้องวงจรปิด  การประมวลผลภาพThe Close Circuit Television (CCTV) is a great device for recording awholesome moving well, it can be used to record events that occur at any time. However, the ability for recording events is good, but the image captured from CCTV, is in terms of quality and sharpness, depends on the performance and features of the CCTV system. A good quality of CCTV is more expensive than low-quality of CCTV in terms of resolution. It caused of installation cost respectively. This research proposed an image enhancement technique for low-quality CCTV, in terms of resolution and sharpness, using the Bicubic-based Pixel Estimation (BPE) technique. The experiments are conducted using by a set of enlarged image. The results show an overall average of PSNR is 19.341 and SSIM is 0.179 which out well than other techniques. Our experimental results show our proposed algorithm outperformed others’ in terms of quality and sharpness.Keywords: Image Enhancement, Interpolation, CCTV, Image Processin