Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral
  Analysis   Convolutional neural network (CNN)-based image denoising methods have been
widely studied recently, because of their high-speed processing capability and
good visual quality. However, most of the existing CNN-based denoisers learn
the image prior from the spatial domain, and suffer from the problem of
spatially variant noise, which limits their performance in real-world image
denoising tasks. In this paper, we propose a discrete wavelet denoising CNN
(WDnCNN), which restores images corrupted by various noise with a single model.
Since most of the content or energy of natural images resides in the
low-frequency spectrum, their transformed coefficients in the frequency domain
are highly imbalanced. To address this issue, we present a band normalization
module (BNM) to normalize the coefficients from different parts of the
frequency spectrum. Moreover, we employ a band discriminative training (BDT)
criterion to enhance the model regression. We evaluate the proposed WDnCNN, and
compare it with other state-of-the-art denoisers. Experimental results show
that WDnCNN achieves promising performance in both synthetic and real noise
reduction, making it a potential solution to many practical image denoising
applications.

3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org