The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.Comment: Accepted to CVPR 2018; Code and data available at https://www.github.com/richzhang/PerceptualSimilarit

Face Recognition on Linear Motion-blurred Image

Most face recognition algorithms are generally capable to achieve a high level of accuracy when the image is acquired under wellcontrolled conditions. The face should be still during the acquisition process; otherwise, the resulted image would be blur and hard for recognition. Enforcing persons to stand still during the process is impractical; extremely likely that recognition should be performed on a blurred image. It is important to understand the relation between the image blur and the recognition accuracy. The ORL Database was used in the study. All images were in PGM format of 92 × 112 pixels from forty different persons, ten images per person. Those images were randomly divided into training and testing datasets with 50-50 ratio. Singular value decomposition was used to extract the features. The images in the testing datasets were artificially blurred to represent a linear motion, and recognition was performed. The blurred images were also filtered using various methods. The accuracy levels of the recognition on the basis of the blurred faces and filtered faces were compared. The performed numerical study suggests that at its best, the image improvement processes are capable to improve the recognition accuracy level by less than five percent

Finger Vein Recognition with Hybrid Deep Learning Approach

Finger vein biometrics is an identification technique based on the vein patterns in fingers, and it has the benefit of being difficult to counterfeit. Due to its high level of security, durability, and performance history, finger vein recognition captures our attention as one of the most significant authentication methods available today. Using a mixed deep learning approach, we investigate the challenge of identifying the finger vein sensor model. Thus far, we use Traditional LSTM architectures for this biometric modality. This work also suggests a brand-new hybrid architecture that shines due to its compactness and a merging with the LSMT layer to be taught. In the experiment, original samples as well as the region of interest data from eight freely available FV-USM datasets are employed. The standard LSTM-based strategy is preferable and produced better outcomes, as seen by the comparison with the earlier approaches. Moreover, the results show that the hybrid CNN and LSTM networks may be used to improve vein detection performance

Image super-resolution for outdoor digital forensics. Usability and legal aspects

This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) through projects TIN2013-43880-R and DPI2016-77869-C2-2-R, the Department of Energy under Grant DE-NA0002520, ONR award N00014-15-1-2735, NSF IDEAS program, DARPA ReImagine.Digital Forensics encompasses the recovery and investigation of data, images, and recordings found in digital devices in order to provide evidence in the court of law. This paper is devoted to the assessment of digital evidence which requires not only an understanding of the scientific technique that leads to improved quality of surveillance video recordings, but also of the legal principles behind it. Emphasis is given on the special treatment of image processing in terms of its handling and explanation that would be acceptable in a court of law. In this context, we propose a variational Bayesian approach to multiple- image super-resolution based on Super-Gaussian prior models that automatically enhances the quality of outdoor video recordings and estimates all the model parameters while preserving the authenticity, credibility and reliability of video data as digital evidence. The proposed methodology is validated both quantitatively and visually on synthetic videos generated from single images and real-life videos and applied to a real-life case of damages and stealing in a private property.Spanish Ministry of Economy and Competitiveness (MINECO) TIN2013-43880-R, DPI2016-77869-C2-2-RDepartment of Energy DE-NA0002520, ONR award N00014-15-1-2735, NSF IDEAS program, DARPA ReImagin

Robust Framework For Digital Image Denoising And Deblurring

Image restoration concerns improving visual quality of a captured image that goes beyond the achievable limit of camera. Recent advancement in imaging and multimedia technology has advocated the interests of image restoration through software, of which applications permeate consumer photography as well as different industries. Unfortunately, the captured images often suffer from degradations, such as blurring, noise, unpleasant artifacts, and more, due to limitations of the imaging system. Despite considerable efforts have been channeled to advance the state-of-the-art methods, surprisingly, these methods are often slow and only designed for handling specific degradation model

Research on Restoration Algorithm of Partially Motion-Blurred Images of Vehicle Based on Video Sequence

图像复原是图像处理技术中一个极具应用价值的重要研究领域，也是学术界和工业界一直以来的研究热点之一。运动模糊图像的复原作为图像复原的一种，主要研究如何从一幅因运动而造成模糊的图像中提取有用信息，复原出清晰的原始图像，具有重要的现实意义。 与全局运动模糊图像的复原相比，局部运动模糊图像的复原不仅需要估计图像退化过程的点扩散函数PSF（pointspreadfunction），利用PSF反卷积去模糊，而且需要检测和提取模糊区域，甚至在某些条件下还需要判别模糊区域的模糊类型。为了有效地复原局部运动模糊的车辆图像，本文从以下几个方面展开了基于多帧的车辆图像复原算法研究： 首先，为了准确、快速地检测和...Image restoration is a very important research field with highly application value in the area of image processing technology, also in academia and industry has been one of the research hotspots. As a kind of image restoration, motion-blurred image restoration which mainly discusses how to extract useful information from motion-blurred image and to restore the original clear image, has very import...学位：工程硕士院系专业：信息科学与技术学院计算机科学系_计算机技术学号：2302009115270

Introductory Computer Forensics

INTERPOL (International Police) built cybercrime programs to keep up with emerging cyber threats, and aims to coordinate and assist international operations for ?ghting crimes involving computers. Although signi?cant international efforts are being made in dealing with cybercrime and cyber-terrorism, ?nding effective, cooperative, and collaborative ways to deal with complicated cases that span multiple jurisdictions has proven dif?cult in practic