2 research outputs found
Learned Compression Artifact Removal by Deep Residual Networks
We propose a method for learned compression artifact removal by
post-processing of BPG compressed images. We trained three networks of
different sizes. We encoded input images using BPG with different QP values. We
submitted the best combination of test images, encoded with different QP and
post-processed by one of three networks, which satisfy the file size and decode
time constraints imposed by the Challenge. The selection of the best
combination is posed as an integer programming problem. Although the visual
improvements in image quality is impressive, the average PSNR improvement for
the results is about 0.5 dB.Comment: Accepted for publication in the CVPR 2018, Challenge on Learned Image
Compression (CLIC), Salt Lake City, Utah, USA, 18 June 2018 and appears in
compression.c
H-OWAN: Multi-distorted Image Restoration with Tensor 1x1 Convolution
It is a challenging task to restore images from their variants with combined
distortions. In the existing works, a promising strategy is to apply parallel
"operations" to handle different types of distortion. However, in the feature
fusion phase, a small number of operations would dominate the restoration
result due to the features' heterogeneity by different operations. To this end,
we introduce the tensor 1x1 convolutional layer by imposing high-order tensor
(outer) product, by which we not only harmonize the heterogeneous features but
also take additional non-linearity into account. To avoid the unacceptable
kernel size resulted from the tensor product, we construct the kernels with
tensor network decomposition, which is able to convert the exponential growth
of the dimension to linear growth. Armed with the new layer, we propose
High-order OWAN for multi-distorted image restoration. In the numerical
experiments, the proposed net outperforms the previous state-of-the-art and
shows promising performance even in more difficult tasks