计量经济分析 = Econometric analysis

The PASCAL Visual Object Classes Challenge

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1240&context=me_pubs

Machine Learning for High-Throughput Stress Phenotyping in Plants

In this correspondence, the authors propose an image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained through SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

/pdf/image-resolution-enhancement-by-using-discrete-and-23qhwdb7nm.pdf

IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition

A wavelet domain image resolution enhancement method is proposed. The method adopts the cycle-spinning methodology adapted for use in the wavelet domain. The perceptual and objective quality of resolution enhanced images compare favourably with recently emerged algorithms in the field.

Wavelet domain image resolution enhancement using cycle-spinning

A new approach to aflatoxin detection in chili pepper by machine vision

Image resolution enhancement using wavelets is a relatively new subject and many new algorithms have been proposed recently. These algorithms assume that the low resolution image is the approximation subband of a higher resolution image and attempts to estimate the unknown detail coefficients to reconstruct a high resolution image. A subset of these recent approaches utilized probabilistic models to estimate these unknown coefficients. Particularly, hidden Markov tree (HMT) based methods using Gaussian mixture models have been shown to produce promising results. However, one drawback of these methods is that, as the Gaussian is symmetrical around zero, signs of the coefficients generated using this distribution function are inherently random, adversely affecting the resulting image quality. In this paper, we demonstrate that, sign information is an important element affecting the results and propose a method to estimate signs of these coefficients more accurately.

/pdf/image-resolution-enhancement-using-wavelet-domain-hidden-5b0l2zwdcc.pdf

Image Resolution Enhancement using Wavelet Domain Hidden Markov Tree and Coefficient Sign Estimation

Cycle spinning, a technique mainly used for wavelet denoising, has also been shown to be successful toward image resolution upscaling in the wavelet domain. We propose a directional variant of the cycle spinning methodology. We obtain estimates of local edge orientation from a wavelet decomposition of the available low-resolution image and use this information to influence the choice of cycle spinning parameters that are employed for resolution upscaling. Our experimental results show that the proposed method outperforms competing methods for a wide range of images offering modest but consistent improvements both in objective as well as subjective terms. Lower computational complexity compared to the conventional cycle spinning is also demonstrated.

https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-14/issue-4/040501/Image-resolution-upscaling-in-the-wavelet-domain-using-directional-cycle/10.1117/1.2061247.pdf

Image resolution upscaling in the wavelet domain using directional cycle spinning

A wavelet-domain image resolution enhancement algorithm which is based on the estimation of detail wavelet coefficients at high resolution scales is proposed. The method exploits wavelet coefficient correlation in a local neighbourhood sense and employs linear least-squares regression to estimate the unknown detail coefficients. Results show that the proposed method is considerably superior to conventional image interpolation techniques, both in objective and subjective terms, while it also compares favourably with competing methods operating in the wavelet domain.

Alptekin Temizel

Papers

Wavelet domain image resolution enhancement using cycle-spinning

A new approach to aflatoxin detection in chili pepper by machine vision

Image Resolution Enhancement using Wavelet Domain Hidden Markov Tree and Coefficient Sign Estimation

Image resolution upscaling in the wavelet domain using directional cycle spinning

Wavelet domain image resolution enhancement