Hao Wen

Bio: Hao Wen is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Magnetic field & Stairstep interpolation. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

A fast edge-directed interpolation algorithm

Abstract: Image interpolation is a method of obtaining a high resolution image from a low resolution image, which is applied to many image processing procedures In order to make the interpolated image having smooth edges and make the interpolation processing fast, we propose a fast edge-directed interpolation algorithm in this paper The proposed method consists of three steps, the determination of nonedge pixels and edge pixels, the bilinear interpolation for nonedge pixels, and the edge-adaptive interpolation for edge pixels The experimental results show that it outperforms some existing interpolation algorithms in terms of image quality and processing speed

Effects of empty bins on image upscaling in capsule endoscopy

Abstract: This paper presents a preliminary study of the effect of empty bins on image upscaling in capsule endoscopy. The presented study was conducted based on results of existing contrast enhancement and interpolation methods. A low contrast enhancement method based on pixels consecutiveness and modified bilinear weighting scheme has been developed to distinguish between necessary empty bins and unnecessary empty bins in the effort to minimize the number of empty bins in the input image, before further processing. Linear interpolation methods have been used for upscaling input images with stretched histograms. Upscaling error differences and similarity indices between pairs of interpolation methods have been quantified using the mean squared error and feature similarity index techniques. Simulation results demonstrated more promising effects using the developed method than other contrast enhancement methods mentioned.

Evaluation of Rounding Functions in Nearest Neighbor Interpolation

Abstract: A novel evaluation study of the most appropriate round function for nearest-neighbor (NN) image interpolation is presented. Evaluated rounding functions are selected among the five rounding rules defined by the Institute of Electrical and Electronics Engineers (IEEE) 754-2008 standard. Both full- and no-reference image quality assessment (IQA) metrics are used to study and evaluate the influence of rounding functions on NN interpolation image quality. The concept of achieved occurrences over targeted occurrences is used to determine the percentage of achieved occurrences based on the number of test images used. Inferential statistical analysis is applied to deduce from a small number of images and draw a conclusion of the behavior of each rounding function on a bigger number of images. Under the normal distribution and at the level of confidence equals to 95%, the maximum and minimum achievable occurrences by each evaluated rounding function are both provided based on the inferential analysis-based experiments.

Normalized Weighting Schemes for Image Interpolation Algorithms.

Abstract: This paper presents and evaluates four weighting schemes for image interpolation algorithms. The first scheme is based on the normalized area of the circle, whose diameter is equal to the minimum side of a tetragon. The second scheme is based on the normalized area of the circle, whose radius is equal to the hypotenuse. The third scheme is based on the normalized area of the triangle, whose base and height are equal to the hypotenuse and virtual pixel length, respectively. The fourth weighting scheme is based on the normalized area of the circle, whose radius is equal to the virtual pixel length-based hypotenuse. Experiments demonstrated debatable algorithm performances and the need for further research.

Image Resolution Enhancement Using Improved Edge Directed Interpolation Algorithm

Abstract: Image resolution enhancement is a process to convert the low-resolution (LR) image into a high-resolution (HR) image. This method is applied in many image processing field. One of the commonly used techniques for image resolution enhancement is interpolation. The results of pixel interpolation can vary significantly depending on the interpolation algorithm. Moreover, the conventional interpolation methods are not efficient to assign accurate interpolation value to the HR edge pixels. Therefore, in this study, we propose an improved edge directed interpolation (EDI) algorithm, which is able to preserve the sharpness of edges. The proposed method is divided into three main steps: edge pixel filtering; bi-cubic interpolation, and EDI. The edge pixels and non-edge pixels are separated by the adaptive edge filtering method. After that bi-cubic interpolation is applied for non-edge pixels. The Lagrange interpolation polynomial is used for bi-cubic interpolation. Finally, an improved EDI is applied to the edge pixels. The proposed method is tested on the several standard grayscale images and compared with the existing methods. According to the evaluation results, the proposed method provides the higher performance of the subjective and objective quality than the standing EDI methods.

Evaluation of Rounding Functions in Nearest-Neighbor Interpolation

Abstract: A novel evaluation study of the most appropriate round function for nearest-neighbor (NN) image interpolation is presented. Evaluated rounding functions are selected among the five rounding rules defined by the Institute of Electrical and Electronics Engineers (IEEE) 754-2008 standard. Both full- and no-reference image quality assessment (IQA) metrics are used to study and evaluate the influence of rounding functions on NN interpolation image quality. The concept of achieved occurrences over targeted occurrences is used to determine the percentage of achieved occurrences based on the number of test images used. Inferential statistical analysis is applied to deduce from a small number of images and draw a conclusion of the behavior of each rounding function on a bigger number of images. Under the normal distribution and at the level of confidence equals to 95%, the maximum and minimum achievable occurrences by each evaluated rounding function are both provided based on the inferential analysis-based experiments.