7,023 research outputs found
Image enhancement from a stabilised video sequence
The aim of video stabilisation is to create a new video sequence where the motions (i.e. rotations, translations) and scale differences between frames (or parts of a frame) have effectively been removed. These stabilisation effects can be obtained via digital video processing techniques which use the information extracted from the video sequence itself, with no need for additional hardware or knowledge about camera physical motion.
A video sequence usually contains a large overlap between successive frames, and regions of the same scene are sampled at different positions. In this paper, this multiple sampling is combined to achieve images with a higher spatial resolution. Higher resolution imagery play an important role in assisting in the identification of people, vehicles, structures or objects of interest captured by surveillance cameras or by video cameras used in face recognition, traffic monitoring, traffic law reinforcement, driver assistance and automatic vehicle guidance systems
Roadmap on optical security
Postprint (author's final draft
Perceiving Unknown in Dark from Perspective of Cell Vibration
Low light very likely leads to the degradation of image quality and even
causes visual tasks' failure. Existing image enhancement technologies are prone
to over-enhancement or color distortion, and their adaptability is fairly
limited. In order to deal with these problems, we utilise the mechanism of
biological cell vibration to interpret the formation of color images. In
particular, we here propose a simple yet effective cell vibration energy (CVE)
mapping method for image enhancement. Based on a hypothetical color-formation
mechanism, our proposed method first uses cell vibration and photoreceptor
correction to determine the photon flow energy for each color channel, and then
reconstructs the color image with the maximum energy constraint of the visual
system. Photoreceptor cells can adaptively adjust the feedback from the light
intensity of the perceived environment. Based on this understanding, we here
propose a new Gamma auto-adjustment method to modify Gamma values according to
individual images. Finally, a fusion method, combining CVE and Gamma
auto-adjustment (CVE-G), is proposed to reconstruct the color image under the
constraint of lightness. Experimental results show that the proposed algorithm
is superior to six state of the art methods in avoiding over-enhancement and
color distortion, restoring the textures of dark areas and reproducing natural
colors. The source code will be released at
https://github.com/leixiaozhou/CVE-G-Resource-Base.Comment: 13 pages, 17 figure
Entropy in Image Analysis III
Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future
A Fast Sand-Dust Image Enhancement Algorithm by Blue Channel Compensation and Guided Image Filtering
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