BLADE: Filter Learning for General Purpose Computational Photography

The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Комп’ютеризована система обробки та аналізу цифрових зображень, отриманих при електронно-променевому зварюванні

The problem of determination of junction lines of objects on images gained at electron-beam welding is considered. Image contrast enhancement is performed by three-stage method based on fuzzy logic. For the determination of welding trajectory locally adaptive approach to segmentation of junction lines of objects and tracing of their contours based on the analysis of image brightness characteristics changes are proposed. Joint сurve is represented analytically with optimal parametrical spline approximation using LSM approximation on each spline link.Розглянуто задачу визначення ліній стику об’єктів на зображеннях отриманих при електронно-променевому зварюванні. Контрастування зображення здійснюється три-етапним методом на основі нечіткої логіки. Для визначення траєкторії зварювання запропоновано локально-адаптивний підхід до сегментації ліній стику об’єктів та відслідковування їх контуру, що базується на аналізі зміни яскравісних характеристик зображення. Траєкторію стику у аналітичному вигляді представлено за допомогою оптимально параметричної сплайн-апроксимації з використанням середньоквадратичних наближень на кожній ланці сплайну