934 research outputs found
Perfectly secure steganography: hiding information in the quantum noise of a photograph
We show that the quantum nature of light can be used to hide a secret message
within a photograph. Using this physical principle we achieve
information-theoretic secure steganography, which had remained elusive until
now. The protocol is such that the digital picture in which the secret message
is embedded is perfectly undistinguishable from an ordinary photograph. This
implies that, on a fundamental level, it is impossible to discriminate a
private communication from an exchange of photographs.Comment: 5 pages, 3 figures + appendix : 5 pages, 6 figure
Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping
High Dynamic Range (HDR) displays can show images with higher color contrast levels and peak luminosities than the common Low Dynamic Range (LDR) displays. However, most existing video content is recorded and/or graded in LDR format. To show LDR content on HDR displays, it needs to be up-scaled using a so-called inverse tone mapping algorithm. Several techniques for inverse tone mapping have been proposed in the last years, going from simple approaches based on global and local operators to more advanced algorithms such as neural networks. Some of the drawbacks of existing techniques for inverse tone mapping are the need for human intervention, the high computation time for more advanced algorithms, limited low peak brightness, and the lack of the preservation of the artistic intentions. In this paper, we propose a fully-automatic inverse tone mapping operator based on mid-level mapping capable of real-time video processing. Our proposed algorithm allows expanding LDR images into HDR images with peak brightness over 1000 nits, preserving the artistic intentions inherent to the HDR domain. We assessed our results using the full-reference objective quality metrics HDR-VDP-2.2 and DRIM, and carrying out a subjective pair-wise comparison experiment. We compared our results with those obtained with the most recent methods found in the literature. Experimental results demonstrate that our proposed method outperforms the current state-of-the-art of simple inverse tone mapping methods and its performance is similar to other more complex and time-consuming advanced techniques
CNN Injected Transformer for Image Exposure Correction
Capturing images with incorrect exposure settings fails to deliver a
satisfactory visual experience. Only when the exposure is properly set, can the
color and details of the images be appropriately preserved. Previous exposure
correction methods based on convolutions often produce exposure deviation in
images as a consequence of the restricted receptive field of convolutional
kernels. This issue arises because convolutions are not capable of capturing
long-range dependencies in images accurately. To overcome this challenge, we
can apply the Transformer to address the exposure correction problem,
leveraging its capability in modeling long-range dependencies to capture global
representation. However, solely relying on the window-based Transformer leads
to visually disturbing blocking artifacts due to the application of
self-attention in small patches. In this paper, we propose a CNN Injected
Transformer (CIT) to harness the individual strengths of CNN and Transformer
simultaneously. Specifically, we construct the CIT by utilizing a window-based
Transformer to exploit the long-range interactions among different regions in
the entire image. Within each CIT block, we incorporate a channel attention
block (CAB) and a half-instance normalization block (HINB) to assist the
window-based self-attention to acquire the global statistics and refine local
features. In addition to the hybrid architecture design for exposure
correction, we apply a set of carefully formulated loss functions to improve
the spatial coherence and rectify potential color deviations. Extensive
experiments demonstrate that our image exposure correction method outperforms
state-of-the-art approaches in terms of both quantitative and qualitative
metrics
High dynamic range color image enhancement using fuzzy logic and bacterial foraging
High dynamic range images contain both the underexposed and the overexposed regions. The enhancement of the underexposed and the overexposed regions is the main concern of this paper. Two new transformation functions are proposed to modify the fuzzy membership values of under and the overexposed regions of an image respectively.For the overexposed regions, a rectangular hyperbolic function is used while for the underexposed regions, an S-function is applied. The shape and range of these functions can be controlled by the parameters involved, which are optimized using the bacterial foraging optimization algorithm so as to obtain the enhanced image. The hue, saturation, and intensity (HSV) color space is employed for the purpose of enhancement, where the hue component is preserved to keep the original color composition intact. This approach is applicable to a degraded image of mixed type. On comparison, the proposed transforms yield better results than the existing transformation functions17 for both the underexposed and the overexposed regions
Optical guidance vidicon test program
A laboratory and field test program was conducted to quantify the optical navigation parameters of the Mariner vidicons. A scene simulator and a camera were designed and built for vidicon tests under a wide variety of conditions. Laboratory tests characterized error sources important to the optical navigation process and field tests verified star sensitivity and characterized comet optical guidance parameters. The equipment, tests and data reduction techniques used are described. Key test results are listed. A substantial increase in the understanding of the use of selenium vidicons as detectors for spacecraft optical guidance was achieved, indicating a reduction in residual offset errors by a factor of two to four to the single pixel level
Linear color correction for multiple illumination changes and non-overlapping cameras
Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms
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