Single image superresolution is a classic and active processing problem, which aims to generate high-resolution (HR) from low-resolution input image. Due the severely under-determined nature of this an effective prior necessary make problem solvable, improve quality generated images. In paper, novel algorithm proposed based on gradient profile sharpness (GPS). GPS edge metric, extracted two description models, i.e., triangle model Gaussian mixture for different kinds profiles. Then,...

Deep probabilistic generative models enable modeling the likelihoods of very high dimensional data. An important application should be ability to detect out-of-distribution (OOD) samples by setting a threshold on likelihood. However, some recent studies show that can, in cases, assign higher certain types OOD samples, making detection rules based likelihood problematic. To address this issue, several methods have been proposed for deep models. In paper, we make observation many these fail...

Over the past two decades, Long Short-Term Memory (LSTM) networks have been used to solve problems that require modeling of long sequence because they can selectively remember certain patterns over a period, thus outperforming traditional feed-forward neural and Recurrent Neural Network (RNN) on learning long-term dependencies. However, LSTM is characterized by feedback dependence, which limits high parallelism general-purpose processors such as CPU GPU. Besides, in terms energy efficiency...

It is an inherently ill-posed problem to separate a single superimposed image into reflection and transmission image. In this letter, novel algorithm proposed based on the prior knowledge that edges of weak are always smoother than most observed objects. To filter out reflection, MRF-EM (Markov Random Field Expectation Maximization) framework proposed. MRF model, data energy function established edge smoothness metric GPS (Gradient Profile Sharpness), spatial formulated using weighted Potts...

In this work, we propose the Generative Latent Flow (GLF), an algorithm for generative modeling of data distribution. GLF uses Auto-encoder (AE) to learn latent representations data, and a normalizing flow map distribution variables that simple i.i.d noise. contrast some other based models, which use various regularizers encourage encoded match prior distribution, our model explicitly constructs mapping between these two distributions, leading better density matching while avoiding over...

The increasing number of digital images and videos has boosted the need no-reference objective image video quality assessment (QA). In this paper, we focus on proposing a perceptual-based blur using feature gradient profile sharpness (GPS). We first build triangle model to represent profiles edge pixels, propose GPS based model. Then metric for is extracted from distribution histogram GPS. Experimental results show that proposed correlates well with perceived blurriness, it can achieve...

In our proposed multi-relay-selection scheme with cyclic delay diversity (CDD), relays spectral efficiency above a threshold are chosen to forward information, and distinct shift is assigned each selected relay. We derive an approximate expression of the end-to-end mutual information for scheme. further analyze outage probability diversity-multiplexing tradeoff (DMT) performance. Numerical results show that can realize full achieve same DMT performance as selection cooperation without need...

In this paper, the concept of data mining was summarized, and its significance that contributes to commerce illustrated as well. Based on characteristic, an important genetic algorithm which is widely used in technology proposed, whose principle status were also expatiated. On other hand, a best employee group based combining information management system for enterprise employees processes steps may solve problem example. Furthermore, problems challenges has be faced with discussed.

Abstract The main objective of the present paper is to further investigate finite‐time synchronization a general complex dynamical network from viewpoint dynamics and control. By utilizing stability theory combined with inequality techniques, several sufficient criteria on are derived analytically. And some effects control parameters speed time also drawn. It shown that gains play an important role in making networks exponentially synchronized. Furthermore, results applied typical...

In this paper, we present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. As foundational text-to-image models rapidly evolve, the demand for robust image-to-image translation grows. Addressing need, MoMA specializes in subject-driven generation. Utilizing open-source, Multimodal Large Language Model (MLLM), train to serve a dual role as both feature extractor and generator. This approach effectively synergizes reference text...

In the field of nerve repair, one major challenge is formation neuroma. However, reports on both promotion regeneration and prevention traumatic neuroma in clinical settings are rare repair. One reasons could be insufficiency follow-up system. We have conducted 33 cases repair using PRGD/PDLLA/β-TCP conduit without any sign adverse reaction, especially no formation. Among them, we selected two as representatives to report this article. The first case was a patient with an upper limb wound...

It is a massively ill-posed problem to separate superimposed image into an object of our interested and interference reflection. Previous studies relied on redundant information introduced by multiple exposure or multi-view configurations in the separation. Later some new methods proposed tailor-made constraints remove reflection specific conditions for single image. However, separated results these always have lot residuals few tone distortions. In this paper, we aim realize clear...

This paper proposes a novel method for robust object tracking. The consists of three different components: short term tracker, an detector, and online model. For the we use advanced Lucas Kanade tracker with bidirectional corner matching to capture frame by frame. Meanwhile, statistical filtering algorithm combined haar-like feature random fern play as detector extract all possible candidates in current Making trajectory information, model decides best target match among candidates. And also...

Unsupervised outlier detection, which predicts if a test sample is an or not using only the information from unlabelled inlier data, important but challenging task. Recently, methods based on two-stage framework achieve state-of-the-art performance this The leverages self-supervised representation learning algorithms to train feature extractor and applies simple detector in space. In paper, we explore possibility of avoiding high cost training distinct for each detection task, instead single...

Robust foreground detection is a fundamental precursor of many video processing applications. Although various approaches were advanced, there still exist factors making very challenging: 1) Dynamic background with gradual brightness changes, camera movement and large amount noises. 2) Sharp illumination changes caused by shadows, light on-off, so on. 3) Real-time requirement for practical systems. To overcome these problems, new approach proposed in this paper. It based on the conventional...

In this work, we investigate the use of normalizing flows to model conditional distributions. particular, our proposed method analyze inverse problems with invertible neural networks by maximizing posterior likelihood. Our uses only a single loss and is easy train. This an improvement on previous that solves similar but which involves combination several terms ad-hoc weighting. addition, provides natural framework incorporate conditioning in flows, therefore, can train network perform...