Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating High Quality Visible Images from SAR Images Using CNNs   We propose a novel approach for generating high quality visible-like images
from Synthetic Aperture Radar (SAR) images using Deep Convolutional Generative
Adversarial Network (GAN) architectures. The proposed approach is based on a
cascaded network of convolutional neural nets (CNNs) for despeckling and image
colorization. The cascaded structure results in faster convergence during
training and produces high quality visible images from the corresponding SAR
images. Experimental results on both simulated and real SAR images show that
the proposed method can produce visible-like images better compared to the
recent state-of-the-art deep learning-based methods.

16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex