74 research outputs found
Recovering Missing Coefficients in DCT-Transformed Images
A general method for recovering missing DCT coefficients in DCT-transformed
images is presented in this work. We model the DCT coefficients recovery
problem as an optimization problem and recover all missing DCT coefficients via
linear programming. The visual quality of the recovered image gradually
decreases as the number of missing DCT coefficients increases. For some images,
the quality is surprisingly good even when more than 10 most significant DCT
coefficients are missing. When only the DC coefficient is missing, the proposed
algorithm outperforms existing methods according to experimental results
conducted on 200 test images. The proposed recovery method can be used for
cryptanalysis of DCT based selective encryption schemes and other applications.Comment: 4 pages, 4 figure
A Compact Linear Programming Relaxation for Binary Sub-modular MRF
We propose a novel compact linear programming (LP) relaxation for binary
sub-modular MRF in the context of object segmentation. Our model is obtained by
linearizing an -norm derived from the quadratic programming (QP) form of
the MRF energy. The resultant LP model contains significantly fewer variables
and constraints compared to the conventional LP relaxation of the MRF energy.
In addition, unlike QP which can produce ambiguous labels, our model can be
viewed as a quasi-total-variation minimization problem, and it can therefore
preserve the discontinuities in the labels. We further establish a relaxation
bound between our LP model and the conventional LP model. In the experiments,
we demonstrate our method for the task of interactive object segmentation. Our
LP model outperforms QP when converting the continuous labels to binary labels
using different threshold values on the entire Oxford interactive segmentation
dataset. The computational complexity of our LP is of the same order as that of
the QP, and it is significantly lower than the conventional LP relaxation
- …