Methods for super-resolution can be classified into three categories: (i) The Interpolation-based methods, (ii) The Reconstruction-based methods (iii) The Learning-based methods. The Learning-based methods usually have the best performance due to the learning process. However, learning-based methods can’t be applied to video super-resolution due to the great computational complexity. We proposed a fast sparsity-based video super-resolution algorithm by utilizing inter-frame information. Firstly, the background can be extracted via existing methods such as Gaussians Mixture Model(GMM) in this paper. Secondly, we construct background and foreground patch dictionaries by randomly sampling patches from high-resolution video. During the process of video super-resolution, only the foreground regions are reconstructed using foreground dictionary via sparse coding. Respectively the background is updated and only changed regions of the background is reconstructed using background dictionary in the same way. Finally, the background and foreground should be fused to get the super-resolution outcome. The experiments show that it makes sparsity-based methods much faster in video super-resolution with approximate, even better, performance.
In this paper, we propose a region of interest-based (ROI-adaptive) fusion algorithm of infrared and visible images by
using the Laplacian Pyramid method. Firstly, we estimate the saliency map of infrared images, and then divide the infrared
image into two parts: the regions of interest (RoI) and the regions of non-interest (nRoI), by normalizing the saliency map.
Visible images are also segmented into two parts by using the Gauss High-pass filter: the regions of high frequency (RoH)
and the regions of low frequency (RoL). Secondly, we down-sampled both the nRoI of infrared image and the RoL of
visible image as the input of next level processing. Finally, we use normalized saliency map of infrared images as the
weighted coefficient to get the basic image on the top level and choose max gray value of the RoI of infrared image and
the RoH of visible image to get the detail image. In this way, our method can keep target feature of infrared image and
texture detail information of visual image at the same time. Experiment results show that such fusion scheme performs
better than the other fusion algorithms both on human visual system and quantitative metrics.
Proc. SPIE. 9273, Optoelectronic Imaging and Multimedia Technology III
KEYWORDS: Visualization, Databases, Image segmentation, Image processing, Control systems, Information technology, Associative arrays, Human vision and color perception, Information visualization, Distributed interactive simulations
The task of salient region detection aims at establishing the most important and informative regions of an image. In this
work, we propose a novel method that tackles such task as a process from superpixel-level locating to pixel-level refining.
Firstly, we over-segment the image into superpixels and compute an affinity matrix to estimate the similarity between
each two superpixels according to both color contrast and space distribution. The matrix is then applied to aggregate
superpixels into several clusters by using affinity propagation. To measure the saliency of each cluster, three parameters
are taken into account including color contrast, cluster compactness and proximity to the focus. We appoint the most
salient one to three clusters as the crude salient region. For the refining step, we regard each selected superpixel as an
influential center. Hence, the saliency value of a pixel is simultaneously determined by all the selected superpixels.
Practically, several Gauss curves are constructed based on the selected superpixels. Pixel-wise saliency value is decided
by the color distinction and spatial distance between one pixel and the curves’ centers. We evaluate our algorithm on the
publicly available dataset with human annotations, and experimental results show that our approach has competitive
Accurate and fast detection of small infrared target has very important meaning for infrared precise guidance, early
warning, video surveillance, etc. Based on human visual attention mechanism, an automatic detection algorithm for
small infrared target is presented. In this paper, instead of searching for infrared targets, we model regular patches that do
not attract much attention by our visual system. This is inspired by the property that the regular patches in spatial domain
turn out to correspond to the spikes in the amplitude spectrum. Unlike recent approaches using global spectral filtering,
we define the concept of local maxima suppression using local spectral filtering to smooth the spikes in the amplitude
spectrum, thereby producing the pop-out of the infrared targets. In the proposed method, we firstly compute the
amplitude spectrum of an input infrared image. Second, we find the local maxima of the amplitude spectrum using cubic
facet model. Third, we suppress the local maxima using the convolution of the local spectrum with a low-pass Gaussian
kernel of an appropriate scale. At last, the detection result in spatial domain is obtained by reconstructing the 2D signal
using the original phase and the log amplitude spectrum by suppressing local maxima. The experiments are performed
for some real-life IR images, and the results prove that the proposed method has satisfying detection effectiveness and
robustness. Meanwhile, it has high detection efficiency and can be further used for real-time detection and tracking.