31,100 research outputs found
SLIC Based Digital Image Enlargement
Low resolution image enhancement is a classical computer vision problem.
Selecting the best method to reconstruct an image to a higher resolution with
the limited data available in the low-resolution image is quite a challenge. A
major drawback from the existing enlargement techniques is the introduction of
color bleeding while interpolating pixels over the edges that separate distinct
colors in an image. The color bleeding causes to accentuate the edges with new
colors as a result of blending multiple colors over adjacent regions. This
paper proposes a novel approach to mitigate the color bleeding by segmenting
the homogeneous color regions of the image using Simple Linear Iterative
Clustering (SLIC) and applying a higher order interpolation technique
separately on the isolated segments. The interpolation at the boundaries of
each of the isolated segments is handled by using a morphological operation.
The approach is evaluated by comparing against several frequently used image
enlargement methods such as bilinear and bicubic interpolation by means of Peak
Signal-to-Noise-Ratio (PSNR) value. The results obtained exhibit that the
proposed method outperforms the baseline methods by means of PSNR and also
mitigates the color bleeding at the edges which improves the overall
appearance.Comment: 6 page
End-to-end Projector Photometric Compensation
Projector photometric compensation aims to modify a projector input image
such that it can compensate for disturbance from the appearance of projection
surface. In this paper, for the first time, we formulate the compensation
problem as an end-to-end learning problem and propose a convolutional neural
network, named CompenNet, to implicitly learn the complex compensation
function. CompenNet consists of a UNet-like backbone network and an autoencoder
subnet. Such architecture encourages rich multi-level interactions between the
camera-captured projection surface image and the input image, and thus captures
both photometric and environment information of the projection surface. In
addition, the visual details and interaction information are carried to deeper
layers along the multi-level skip convolution layers. The architecture is of
particular importance for the projector compensation task, for which only a
small training dataset is allowed in practice. Another contribution we make is
a novel evaluation benchmark, which is independent of system setup and thus
quantitatively verifiable. Such benchmark is not previously available, to our
best knowledge, due to the fact that conventional evaluation requests the
hardware system to actually project the final results. Our key idea, motivated
from our end-to-end problem formulation, is to use a reasonable surrogate to
avoid such projection process so as to be setup-independent. Our method is
evaluated carefully on the benchmark, and the results show that our end-to-end
learning solution outperforms state-of-the-arts both qualitatively and
quantitatively by a significant margin.Comment: To appear in the 2019 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). Source code and dataset are available at
https://github.com/BingyaoHuang/compenne
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