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Video change detection, which plays an important role in computer vision, is far from being well resolved due to the complexity of diverse scenes in real world. Most of the current methods are designed based on hand-crafted features and perform well in some certain scenes but may fail on others. This paper puts up forward a deep learning based method to automatically fuse multiple basic detections into an optimal

Applying a deep convolutional neural network CNN to no reference image quality assessment (NR-IQA) is a challenging task due to the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method–the Deep Blind image Quality Assessment predictor (DeepBQA)– adopts two-step training stages to avoid overfitting. In the first stage, a ground-truth objective error map is generated and used as a proxy training target.

There is growing interest in the use of coded aperture imaging systems for a variety of applications. Using an analysis framework based on mutual information, we examine the fundamental limits of such systems—and the associated optimum aperture coding—under simple but meaningful propagation and sensor models. Among other results, we show that when SNR is high and thermal noise dominates shot noise, spectrally-flat masks, which have 50% transmissivity, are optimal, but that when shot noise dominates thermal noise, randomly generated masks with lower transmissivity offer greater performance.

Cross-modal sketch-photo recognition is of vital importance
in law enforcement and public security. Most existing methods
are dedicated to bridging the gap between the low-level
visual features of sketches and photo images, which is limited
due to intrinsic differences in pixel values. In this paper, based
on the intuition that sketches and photo images are highly correlated
in the semantic domain, we propose to jointly utilize
the low-level visual features and high-level facial attributes to

Cross-modal sketch-photo recognition is of vital importance
in law enforcement and public security. Most existing methods
are dedicated to bridging the gap between the low-level
visual features of sketches and photo images, which is limited
due to intrinsic differences in pixel values. In this paper, based
on the intuition that sketches and photo images are highly correlated
in the semantic domain, we propose to jointly utilize
the low-level visual features and high-level facial attributes to

Many image restoration problems are ill-posed in nature, hence, beyond the input image, most existing methods rely on a carefully engineered image prior, which enforces some local image consistency in the recovered image. How tightly the prior assumptions are fulfilled has a big impact on the resulting task performance. To obtain more flexibility, in this work, we proposed to design the image prior in a data-driven manner. Instead of explicitly defining the prior, we learn it using deep generative models.

We develop new efficient online algorithms for detecting transient sparse signals in TEM video sequences, by adopting the recently developed framework for sequential detection jointly with online convex optimization [1]. We cast the problem as detecting an unknown sparse mean shift of Gaussian observations, and develop adaptive CUSUM and adaptive SSRS procedures, which are based on likelihood ratio statistics with post-change mean vector being online maximum likelihood estimators with ℓ1. We demonstrate the meritorious performance of our algorithms for TEM imaging using real data.

Low-resolution (LR) face identification is always a challenge in computer vision. In this paper, we propose a new LR face recognition and reconstruction method using deep canonical correlation analysis (DCCA). Unlike linear CCA-based methods, our proposed method can learn flexible nonlinear representations by passing LR and high-resolution (HR) image principal component features through multiple stacked layers of nonlinear transformation.

Texture segmentation still constitutes an on-going challenge, especially when processing large-size images.
Recently, procedures integrating a scale-free (or fractal)wavelet-leader model allowed the problem to be reformulated in a convex optimization framework by including a TV penalization. In this case, the TV penalty plays