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  • 大小: 3.87MB
    文件類型: .zip
    金幣: 2
    下載: 1 次
    發布日期: 2023-11-09
  • 語言: Python
  • 標簽: DnCNN,??python??

資源簡介

這個存儲庫包含使用深度學習對高分辨率圖像進行分解的工作的代碼。目前最先進的方法,如BM3D,KSVD和非本地手段確實能夠產生高質量的去噪效果。但是當圖像的大小變得非常高時,例如。 4000 x 80000像素,那些高質量的結果以高計算時間為代價。這個耗時的因素可以作為一個動機來提出一個模型,可以在更短的時間內提供可比較的結果,如果不是更好的話。因此,我使用了一種深度學習方法,它會自動嘗試學習將噪聲圖像映射到其去噪版本的功能。

資源截圖

代碼片段和文件信息


from?__future__?import?print_function

import?os
import?sys
import?timeit

import?numpy

import?theano
import?theano.tensor?as?T
from?theano.sandbox.rng_mrg?import?MRG_RandomStreams?as?RandomStreams

from?logistic_sgd?import?load_data
from?utils?import?tile_raster_images

try:
????import?PIL.Image?as?Image
except?ImportError:
????import?Image


class?dA(object):
????“““Denoising?Auto-Encoder?class?(dA)

????A?denoising?autoencoders?tries?to?reconstruct?the?input?from?a?corrupted
????version?of?it?by?projecting?it?first?in?a?latent?space?and?reprojecting
????it?afterwards?back?in?the?input?space.?Please?refer?to?Vincent?et?al.2008
????for?more?details.?If?x?is?the?input?then?equation?(1)?computes?a?partially
????destroyed?version?of?x?by?means?of?a?stochastic?mapping?q_D.?Equation?(2)
????computes?the?projection?of?the?input?into?the?latent?space.?Equation?(3)
????computes?the?reconstruction?of?the?input?while?equation?(4)?computes?the
????reconstruction?error.

????..?math::

????????\tilde{x}?~?q_D(\tilde{x}|x)?????????????????????????????????????(1)

????????y?=?s(W?\tilde{x}?+?b)???????????????????????????????????????????(2)

????????x?=?s(W‘?y??+?b‘)????????????????????????????????????????????????(3)

????????L(xz)?=?-sum_{k=1}^d?[x_k?\log?z_k?+?(1-x_k)?\log(?1-z_k)]??????(4)

????“““

????def?__init__(
????????self
????????numpy_rng
????????theano_rng=None
????????input=None
????????n_visible=784
????????n_hidden=500
????????W=None
????????bhid=None
????????bvis=None
????):
????????“““
????????Initialize?the?dA?class?by?specifying?the?number?of?visible?units?(the
????????dimension?d?of?the?input?)?the?number?of?hidden?units?(?the?dimension
????????d‘?of?the?latent?or?hidden?space?)?and?the?corruption?level.?The
????????constructor?also?receives?symbolic?variables?for?the?input?weights?and
????????bias.?Such?a?symbolic?variables?are?useful?when?for?example?the?input
????????is?the?result?of?some?computations?or?when?weights?are?shared?between
????????the?dA?and?an?MLP?layer.?When?dealing?with?SdAs?this?always?happens
????????the?dA?on?layer?2?gets?as?input?the?output?of?the?dA?on?layer?1
????????and?the?weights?of?the?dA?are?used?in?the?second?stage?of?training
????????to?construct?an?MLP.

????????:type?numpy_rng:?numpy.random.RandomState
????????:param?numpy_rng:?number?random?generator?used?to?generate?weights

????????:type?theano_rng:?theano.tensor.shared_randomstreams.RandomStreams
????????:param?theano_rng:?Theano?random?generator;?if?None?is?given?one?is
?????????????????????generated?based?on?a?seed?drawn?from?‘rng‘

????????:type?input:?theano.tensor.TensorType
????????:param?input:?a?symbolic?description?of?the?input?or?None?for
??????????????????????standalone?dA

????????:type?n_visible:?int
????????:param?n_visible:?number?of?visible?units

????????:type?n_hidden:?int
????????:param?n_hidden:??number?of?hidden?units

????????:type?W:?theano.tensor.TensorType
????????:param?W:?Theano?variable?pointing?to?a?set?o

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2017-12-14?10:45??image-denoising-master\
?????文件????????2320??2017-12-14?10:45??image-denoising-master\README.md
?????文件???????13234??2017-12-14?10:45??image-denoising-master\dA.py
?????目錄???????????0??2017-12-14?10:45??image-denoising-master\data\
?????文件???????86860??2017-12-14?10:45??image-denoising-master\data\band1_denoised1.png
?????文件???????89019??2017-12-14?10:45??image-denoising-master\data\band1_denoised2.png
?????文件???????96255??2017-12-14?10:45??image-denoising-master\data\band1_noisy1.png
?????文件???????95355??2017-12-14?10:45??image-denoising-master\data\band1_noisy2.png
?????文件???????29920??2017-12-14?10:45??image-denoising-master\denoise_function.py
?????目錄???????????0??2017-12-14?10:45??image-denoising-master\graphs\
?????文件???????20564??2017-12-14?10:45??image-denoising-master\graphs\patches_plot.png
?????文件???????14858??2017-12-14?10:45??image-denoising-master\graphs\psnr_plot.png
?????文件???????21213??2017-12-14?10:45??image-denoising-master\graphs\variation_with_no_of_hidden_layers1.png
?????文件???????21676??2017-12-14?10:45??image-denoising-master\graphs\variation_with_size_of_hidden_layers.png
?????文件???????16224??2017-12-14?10:45??image-denoising-master\logistic_sgd.py
?????文件???????13634??2017-12-14?10:45??image-denoising-master\mlp.py
?????目錄???????????0??2017-12-14?10:45??image-denoising-master\paper\
?????文件?????2633933??2017-12-14?10:45??image-denoising-master\paper\Final_icmla_poster(1)_jp.jpg
?????文件?????1440465??2017-12-14?10:45??image-denoising-master\paper\Final_paper.pdf

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