7,189 research outputs found
The Perception-Distortion Tradeoff
Image restoration algorithms are typically evaluated by some distortion
measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify
perceived perceptual quality. In this paper, we prove mathematically that
distortion and perceptual quality are at odds with each other. Specifically, we
study the optimal probability for correctly discriminating the outputs of an
image restoration algorithm from real images. We show that as the mean
distortion decreases, this probability must increase (indicating worse
perceptual quality). As opposed to the common belief, this result holds true
for any distortion measure, and is not only a problem of the PSNR or SSIM
criteria. We also show that generative-adversarial-nets (GANs) provide a
principled way to approach the perception-distortion bound. This constitutes
theoretical support to their observed success in low-level vision tasks. Based
on our analysis, we propose a new methodology for evaluating image restoration
methods, and use it to perform an extensive comparison between recent
super-resolution algorithms.Comment: CVPR 2018 (long oral presentation), see talk at:
https://youtu.be/_aXbGqdEkjk?t=39m43
Non-local Attention Optimized Deep Image Compression
This paper proposes a novel Non-Local Attention Optimized Deep Image
Compression (NLAIC) framework, which is built on top of the popular variational
auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations
in the encoders and decoders for both image and latent feature probability
information (known as hyperprior) to capture both local and global
correlations, and apply attention mechanism to generate masks that are used to
weigh the features for the image and hyperprior, which implicitly adapt bit
allocation for different features based on their importance. Furthermore, both
hyperpriors and spatial-channel neighbors of the latent features are used to
improve entropy coding. The proposed model outperforms the existing methods on
Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional
(e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and
MS-SSIM distortion metrics
Weak Lensing Mass Reconstruction using Wavelets
This paper presents a new method for the reconstruction of weak lensing mass
maps. It uses the multiscale entropy concept, which is based on wavelets, and
the False Discovery Rate which allows us to derive robust detection levels in
wavelet space. We show that this new restoration approach outperforms several
standard techniques currently used for weak shear mass reconstruction. This
method can also be used to separate E and B modes in the shear field, and thus
test for the presence of residual systematic effects. We concentrate on large
blind cosmic shear surveys, and illustrate our results using simulated shear
maps derived from N-Body Lambda-CDM simulations with added noise corresponding
to both ground-based and space-based observations.Comment: Accepted manuscript with all figures can be downloaded at:
http://jstarck.free.fr/aa_wlens05.pdf and software can be downloaded at
http://jstarck.free.fr/mrlens.htm
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