Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
A Comparative Study of Histogram Equalization Based Image Enhancement Techniq...Shahbaz Alam
Four widely used histogram equalization techniques for image enhancement namely GHE, BBHE, DSIHE, RMSHE are discussed. Some basic definitions and notations are also attached. All analysis are done by using MATLAB . Pictures are taken from the book "Digital Image Processing" by Rafael C. Gonzalez and Richard E. Woods. The presentation slide was made for my B.Sc project purpose.
Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Histogram equalization can be used to improve the visual appearance of an image. Peaks in the image histogram (indicating commonly used grey levels) are widened, while the valleys are compressed.
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
Study on Contrast Enhancement with the help of Associate Regions Histogram Eq...IJSRD
Histogram equalization is an uncomplicated and extensively used image distinction enhancement technique. The crucial drawback of histogram equalization is it transforms the brightness of the image. To overcome this drawback, different histogram Equalization methods have been projected. These methods protect the brightness on the result image but, do not have a usual look. Therefore this paper is an attempt to bridge the gap and results after the processed Associate regions are collected into one image. The mock-up result explains that the algorithm can not only improve image information successfully but also remain the imaginative image luminance well enough to make it likely to be used in video arrangement directly.
A Comparative Study of Histogram Equalization Based Image Enhancement Techniq...Shahbaz Alam
Four widely used histogram equalization techniques for image enhancement namely GHE, BBHE, DSIHE, RMSHE are discussed. Some basic definitions and notations are also attached. All analysis are done by using MATLAB . Pictures are taken from the book "Digital Image Processing" by Rafael C. Gonzalez and Richard E. Woods. The presentation slide was made for my B.Sc project purpose.
Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Histogram equalization can be used to improve the visual appearance of an image. Peaks in the image histogram (indicating commonly used grey levels) are widened, while the valleys are compressed.
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
Study on Contrast Enhancement with the help of Associate Regions Histogram Eq...IJSRD
Histogram equalization is an uncomplicated and extensively used image distinction enhancement technique. The crucial drawback of histogram equalization is it transforms the brightness of the image. To overcome this drawback, different histogram Equalization methods have been projected. These methods protect the brightness on the result image but, do not have a usual look. Therefore this paper is an attempt to bridge the gap and results after the processed Associate regions are collected into one image. The mock-up result explains that the algorithm can not only improve image information successfully but also remain the imaginative image luminance well enough to make it likely to be used in video arrangement directly.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image Restoration UsingNonlocally Centralized Sparse Representation and histo...IJERA Editor
Due to the degradation of observed image the noisy, blurred, distorted image can be occurred .To restore the image informationby conventional modelsmay not be accurate enough for faithful reconstruction of the original image. I propose the sparse representations to improve the performance of based image restoration. In this method the sparse coding noise is added for image restoration, due to this image restoration the sparse coefficients of original image can be detected. The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, fordenoising the image here we use the histogram clipping method by using histogram based sparse representation to effectively reduce the noise and also implement the TMR filter for Quality image. Various types of image restoration problems, including denoising, deblurring and super-resolution, validate the generality and state-of-the-art performance of the proposed algorithm.
3 ijaems nov-2015-6-development of an advanced technique for historical docum...INFOGAIN PUBLICATION
In this paper, technique used for historical document preservation is explored. In this paper a noise estimation technique is applied to know noise standard deviation. We first estimate or detect level of noise present in noisy images by selecting weak textured patches in image on the basis of gradient matrix and its statistical properties, then eliminate that noise through non local means(NLM) denoising technique that will use estimated noise level as filtering parameter for eliminating noise from the image. This technique is based on weighted average of the similar pixels in historical image. Non local means techniques removes noise from images without taking care of noise level ,it is mandatory to take care of noise level for best preserving Historical document images.
IMAGE ENHANCEMENT IN CASE OF UNEVEN ILLUMINATION USING VARIABLE THRESHOLDING ...ijsrd.com
Uneven illumination always affects the visual quality images which results in poor understanding about the content of the images. There is no accepted universal image enhancement algorithm or specific criteria which can fulfill user needs. The processed image may be very different with the original image in the visual effects, but it also may be similar to the original image [1]. It will be a developing tradition to integrate the advantage of various algorithms to practical application to image enhancements [2]. Zhang et al. [3] presents an adaptive image contrast enhancement method. The proposed method is based on a local gamma correction piloted by histogram analysis. In this paper , to avoid uneven Illuminance image is divided into different segments . It works locally to decrease contrast as if we perform enhancement techniques globally on portions which are already bright then this gives poor results. Enhancement techniques are applied only to those dark portions. We need accurate method that not only enhance the image but also preserve the information.
Image Denoising by using Modified SGHP Algorithm IJECEIAES
In real time applications, image denoising is a predominant task. This task makes adequate preparation for images looks prominent. But there are several denoising algorithms and every algorithm has its own distinctive attribute based upon different natural images. In this paper, we proposed a perspective that is modified parameter in S-Gradient Histogram Preservation denoising method. S-Gradient Histogram Preservation is a method to compute the structure gradient histogram from the noisy observation by taking different noise standard deviations of different images. The performance of this method is enumerated in terms of peak signal to noise ratio and structural similarity index of a particular image. In this paper, mainly focus on peak signal to noise ratio, structural similarity index, noise estimation and a measure of structure gradient histogram of a given image.
Semantic image completion and enhancement using Deep LearningPriyansh Saxena
In real-life applications, certain images utilized are corrupted in which the image pixels are damaged or missing, which increases the complexity of computer vision tasks. In this paper, a deep learning architecture is proposed to deal with image completion and enhancement. Generative Adversarial Networks (GAN), has been turned out to be helpful in picture completion tasks. Therefore, in GANs, Wasserstein GAN architecture is used for image completion which creates the coarse patches to filling the missing region in the distorted picture, and the enhancement network will additionally refine the resultant pictures utilizing residual learning procedures and hence give better complete pictures for computer vision applications. Experimental outcomes show that the proposed approach improves the Peak Signal to Noise ratio and Structural Similarity Index values by 2.45% and 4% respectively when compared to the recently reported data.
An Improved Image Fusion Scheme Based on Markov Random Fields with Image Enha...Editor IJCATR
Image fusion is an important field in many image processing and analysis tasks in which fusion image data are acquired
from multiple sources. In this paper, we investigate the Image fusion of remote sensing images which are highly corrupted by salt and
pepper noise. In our paper we propose an image fusion technique based Markov Random Field (MRF). MRF models are powerful
tools to analyze image characteristics accurately and have been successfully applied to a large number of image processing
applications like image segmentation, image restoration and enhancement, etc.,. To de-noise the corrupted image we propose a
Decision based algorithm (DBA). DBA is a recent powerful algorithm to remove high-density Salt and Pepper noise using sheer
sorting method is proposed. Previously many techniques have been proposed to image fusion. In this paper experimental results are
shown our proposed Image fusion algorithm gives better performance than previous techniques.
Image Restitution Using Non-Locally Centralized Sparse Representation ModelIJERA Editor
Sparse representation models uses a linear combination of a few atoms selected from an over-completed
dictionary to code an image patch which have given good results in different image restitution applications. The
reconstruction of the original image is not so accurate using traditional models of sparse representation to solve
degradation problems which are blurring, noisy, and down-sampled. The goal of image restitution is to suppress
the sparse coding noise and to improve the image quality by using the concept of sparse representation. To
obtain a good sparse coding coefficients of the original image we exploit the image non-local self similarity and
then by centralizing the sparse coding coefficients of the observation image to those estimates. This non-locally
centralized sparse representation model outperforms standard sparse representation models in all aspects of
image restitution problems including de-noising, de-blurring, and super-resolution.
Intensity Preserving Cast Removal in Color Images Using Particle Swarm Optimi...IJECEIAES
In this paper, we present an optimal image enhancement technique for color cast images by preserving their intensity. There are methods which improves the appearance of the affected images under different cast like red, green, blue etc but up to some extent. The proposed color cast method is corrected by using transformation function based on gamma values. These optimal values of gamma are obtained through particle swarm optimization (PSO). This technique preserves the image intensity and maintains the originality of color by satisfying the modified gray world assumptions. For the performance analysis, the image distance metric criteria of CIELAB color space is used. The effectiveness of the proposed approach is illustrated by testing the proposed method on color cast images. It has been found that distance between the reference image and the corrected proposed image is negligible. The calculated value of image distance depicts that the enhanced image results of the proposed algorithm are closer to the reference images in comparison with other existing methods.
Denoising Process Based on Arbitrarily Shaped WindowsCSCJournals
Many factors, such as moving objects, introduce noise in digital images. The presence of noise affects image quality. The image denoising process works on reconstructing a noiseless image and improving its quality. When an image has an additive white Gaussian noise (AWGN) then denoising becomes a challenging process. In our research, we present an improved algorithm for image denoising in the wavelet domain. Homogenous regions for an input image are estimated using a region merging algorithm. The local variance and wavelet shrinkage algorithm are applied to denoise each image patch. Experimental results based on peak signal to noise ratio (PSNR) measurements showed that our algorithm provided better results compared with a denoising algorithm based on a minimum mean square error (MMSE) estimator.
OTSU Thresholding Method for Flower Image Segmentationijceronline
Segmentation is basic process in image processing. It always produces an effective result for next process. In this paper, we proposed the flower image segmentation. Oxford flower collection is used for segmentation.Different segmentation techniques are available. Different techniques and algorithm are developed to describe the segmentation.We proposed a OTSU thresholding technique for flower image segmentation in this paper. which gives good result as compared with the other methods and simple also.Segmentation subdivide the image into different parts.firstly, segmentation techniques and then otsu thresholding method described in this paper.CIE L*a*b color space is used in thresholding for better results.Thresholding apply seperatly on each L, a and b component. accordingly the features can be extracted like shape, color, texture etc. finally, results with the flower images are shown.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Comparison of Histogram Equalization Techniques for Image Enhancement of Gray...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
1. Topic K - Image manipulation with generative adversarial networks (GANs)
Final report
Khalil BERGAOUI
khalil.bergaoui@student.ecp.fr
Azza BEN FARHAT
azza.ben-farhat@student.ecp.fr
1. Abstract
In this report, we will present our work as an extension
of the SinGAN method (2) to the problem of image de-
noising. We will therefore begin by presenting the method
and its application as in the paper (2). Then we will re-
produce some of the presented results and compare them to
our obtained results. Finally, we will formulate the image
denoising problem in the domain of Additive White Gaus-
sian Noise (AWGN) and will present the two approaches
we have adopted. In both approaches, we will compare the
denoising performance to a state-of-the-art algorithm (1) us-
ing the PSNR (Peak Signal to Noise Ratio) as a distortion
metric.
2. SinGAN presentation
Capturing the distribution of highly diverse image con-
tents often requires conditioning the generative model by
training it on a specific task or image class category. In
this context, the authors of (2) propose an approach to deal
with generating general natural images that contain com-
plex structures and textures, without the need to rely on the
existence of a database of images from the same class. In
fact, by proposing SinGAN, the generation problem can be
formulated as follows:
Given a single natural image x, learn the image’s patch
statistics across multiple scales to generate, from noise, a
synthetic realistic image y which preserves the patch
distribution in x while creating new structures.
The authors describe the training pipeline, for a N + 1-
scale network, as follows :
- Each scale 0 ≤ n ≤ N is trained using a sum of the
adversarial loss, reflecting a competition between the gen-
erator Gn and the discriminator Dn, and a reconstruction
loss term :
min
Gn
max
Dn
Ladv(Dn, Gn) + αLrec(Gn)
where Lrec = ||In −Gopt
n ||2
is the pixel to pixel distance
between the input In at scale n (obtained by downsampling
Figure 1: Multiscale pipeline(2)
the original training image) and the generator’s output Gopt
n .
The noise map used to compute the reconstruction loss is
specified as :{zrec
N , zrec
N−1, ..., zrec
0 } = {z∗
, 0, ..., 0} = zopt
and kept constant during training.
- Training starts from the coarsest scale up until the finest
scale, where the current scale is initialized using the learned
weights of the previous scale’s network. Additionally, when
n < N, the output of the generator Gn is given by :
x̃n = Gn(zn, (x̃n+1)up
)
where (x̃n+1)up
is the upsampled output of the previous
scale. (For n=N, x̃N = GN (zN )).
Using this pipeline, the model is able to learn effectively
the patch statistics from a single training image and this
method can successfully be used for a number of interesting
applications as will be detailed in the next section.
3. Reproduced results
In this section, we will reproduce some quantitative and
qualitative results presented in the paper. This step was cru-
cial for us in order to better understand the method and be-
come familiar with the implementation.
3.1. Training with a different number of scales
We tried to study the effect of training with fewer scales.
We can see from the paper’s results (figure 1.a) and from
2. ours (figure 1.b) that when we generate samples from mod-
els learnt with a small number of scales (2, 4 and 5), only
general textures are captured. As the number of scales in-
creases, larger structures emerge, as well as the global ar-
rangement of objects in the scene. This is due to the size
of the effective receptive field that is lowest at the coarsest
levels, allowing to capture only fine textures.
(a) paper results
(b) our results
Figure 2: Training with a different number of scales.
3.2. Generating from different scales
The multi-scale architecture of SinGAN allows us to
choose the scale from which we want to generate samples.
At test time, if we generate from the coarsest scale N, we
use random noise as an input. However, if we want to gen-
erate from finer scales n < N, the input used is a downsam-
pled version of the original image. As we can see in figure 2,
changing the scale from which we generate has an impact
on the results. For example, generating from the scale N
does not preserve the global structures of the training image
(the Zebra can have 5 legs, the tree can have 2 trunks,etc),
while generating from finer scales N − 1 and N − 2 allows
to preserve the global structure and changes only fine details
(the shape and pose of the Zebra are preserved and only its
stripe texture changes).
Similar effects are also present in the figures we repro-
duced (images of the cows and the stone) but they are less
interpretable compared to the example of the Zebra.
(a) paper results
(b) our results
Figure 3: Generating random samples from different scales.
3.3. Super resolution
This application consists in increasing the resolution of
an image by a chosen factor s. To do so, SinGAN is trained
on a low resolution input image using a pyramid scale r =k
√
s for k ∈ N. This way, at test time, we upsample the
low-resolution input image by a factor r and we inject it to
the generator at the finest scale. We repeat this k times to
obtain the high-resolution image.
We reproduced the super resolution results of the paper,
where we increase the resolution of the input image by a
factor 4, as shown in figure 4. Our results are very similar
to the paper’s.
Figure 4: Super resolution, with a resolution factor of 4.
The low resolution image is on the left, the paper’s result is
in the middle and ours is on the right.
2
3. 3.4. Paint-to-Image
Transforming a Paint into an Image can be achieved by
training SinGAN on a target image, then feeding a down-
sampled paint into one the coarsest levels (N −1 or N −2).
As we can see in figure 5, we obtain good results where
the output preserves the global structure of the painting and
generates fine details from the target image.
Figure 5: Paper results.
We can also see on figure 6 the impact of the scale to
which we feed the downsampled clipart. In fact, one can
notice that the chosen scale has an impact on the quality of
the output and on the preservation of the global shapes. In
our example, scale N − 2 seems to lead to the result that is
closest to the training image.
Figure 6: Our results.
4. Application to denoising
PS: As of this section, we will adopt the notation used
in the code implementation rather than the paper, in a sense
that we will refer to the coarsest scale as the scale with the
lowest index.
In this section, we will apply SinGAN to the following
problem, assuming a Gaussian Noise n ,→ N(0, σ2
) :
Given a noisy image x = y + n, Generate, the underlying
clean image y
Experimentally, we will proceed by training SinGAN
on the image yt, then during inference, we will construct
a noisy version x = yt + n(σ) and feed it to a generator
Gn at some scale n of the SinGAN. We will conduct
two types of experiments, first yt will represent the clean
image y, then a noisy version of y. The output of the
n-scale Gn will be considered as the denoised image and
will be compared to the original clean image y. We will
conduct the experiments for a wide range of noise standard
deviation σ values in order to assess the limitations of the
application. Since SinGAN trains by learning the patch
statistics of the image, the question we would like to answer
is the following :
Could we benefit from the multi-scale architecture in or-
der to restore the image by separating noise from the learned
patch statistics ?
4.1. Train on a clean image
We start by considering training the model on a clean im-
age (without adding noise) and we use a 6-scale architecture
(the corresponding scale factor parameter is 0.75). At test
time, we choose a scale n and feed a downsampled noisy
image to the generator Gn. We plot in figure 7 the Peak
Signal to Noise Ratio for different values of σ, for different
scales:
Figure 7: Denoising results for different training scales with
a 6-scale architecture. The order of the scales indicated in
the figure is as follows: gen start scale = 6 corresponds to
the finest scale and gen start scale = 0 corresponds to the
coarsest scale.
We observe that denoising is more efficient starting from
the finest scales. Also, intermediate scales seem to general-
ize better for larger noise, and that it leads to the best results
when σ gets large (≥ 35). Note that, in the above figure,
we added G(zopt
) as our baseline as it theoretically repre-
sents the best reconstruction candidate when training on the
clean image, because the reconstruction loss Lrec is exactly
computed using the noise map zopt
as described in section
1 of our report.
In a next step, we trained the model on the same clean
input image using a 13-scale architecture (the correspond-
ing scale factor parameter is 0.85). The below figure shows
that the best results are obtained at intermediate scales (6
and 7). Hence, it seems that increasing the training scales
3
4. (by increasing the scale factor) improves the denoising per-
formance.
Figure 8: Denoising results for different training scales with
a 13-scale architecture. The order of the scales indicated on
the figure is as follows: gen start scale = 13 corresponds to
the finest scale and gen start scale = 0 corresponds to the
coarsest scale.
Finally, we compared the improved PSNR, obtained us-
ing the 13-architecture, with the performance of a-state-of-
the-art denoising algorithm: BM3D(1) on the same noisy
image. Table 1 shows the results where we can see that
BM3D slightly outperforms SinGAN and that the difference
between the PSNRs decreases when σ increases (the image
is more noisy).
σ SinGAN BM3D
10 26.35 30.05
20 24.80 26.02
30 23.47 24.04
40 22.08 22.94
Table 1: PSNR(dB) comparison
However, depending on the size of the training image,
SinGAN could take up to 1.5h, while BM3D takes only
about 5s (in our example) to perform denoising. Moreover,
in practice, we do not have access to the clean image. That
is why we considered a second approach in which we train
the model on a noisy image directly.
4.2. Train on a noisy image
In this section, we consider a more realistic study case
in which we do not have access to the clean image as in
the previous section. The training image therefore consists
of a noisy image obtained by adding AWGN of standard
deviation σ = 30 and we proceed by down-sampling the
image which initially has a 367x585 resolution to obtain a
training image of resolution 157x250. The advantages of
such approach are three fold :
- Down-sampling reduces the noise level: In fact, down-
sampling the image can be equivalent to computing a new
image by averaging neighbour pixels to reduce the resolu-
tion. Such averaging operation helps reduce the noise level.
In particular, in the context of gaussian noise, and assum-
ing independence between the individual pixels, it can be
shown that the noise standard deviation σdown in the down-
sampled image can be expressed as :
σdown = σ ∗
q
Ndown
N where N and Ndown are respec-
tively the number of pixels in the original image and in the
down-sampled one.
- Training time decreases with the resolution of the train-
ing image (this could be interpreted as a result of the com-
putation of the pixel-to-pixel reconstruction loss Lrec). In
fact, we report (Figure Appendix D) the time that takes to
train a single scale on 2000 epochs (using the default config-
uration) for various settings of training image resolutions.
- During inference, we can take advantage of the Super
Resolution mode in order to compensate the down-sampling
operation.
In our case, we compare the results obtained using this
approach, with and without Super Resolution at test time.
The used architecture contains 12-scales and as expected,
the network overfits the training image and learns the noise
pattern. However, interestingly, this occurs at the level of
the finest scales. In fact, we can visualize the output of
the nth
Generator from n = 1 (coarsest scale) to n = 12
(Finest scale). We observe (Figure Appendix B) as we move
from the coarsest to the finest scale, more image details are
learned but also the noise! We find that the 5th
scale pro-
vides the best tradeoff between enough image details and
relatively low noise levels (the image appears smooth but
somewhat blurry).
Quantitatively, this effect can be seen by plotting the
PSNR as a function of the scale:
Figure 9: PSNR vs Scale.
The peak is obtained for the generator G5 (similarly
to the qualitative result) with a value PSNR = 20.9dB.
4
5. Then, we attempt to increase our performance by applying
Super-Resolution as described in section 3.3 (this time only
during inference). By varying the super resolution factor,
the best results were obtained using the same generator G5
using a super-resolution factor ≈ 1.5:
Figure 10: PSNR vs Super-Resolution Factor.
As a result, we obtain an improvement of 1.2dB com-
pared to the situation without using SR, giving us a value
PSNR = 22.10dB. Eventhough we managed to im-
prove our denoising efficiency using SR, we are still out-
performed by state-of-the-art algorithms: For instance
BM3D(1) scores, for the same example (and same σ = 30)
a PSNR = 26.97dB.
5. Conclusion
As we have shown in the previous sections, we have
managed to successfully reproduce most of the results pre-
sented in the paper. Moreover, with respect to applying
SiNGAN to denoising as defined in our report, our denois-
ing methods are still far from state-of-the-art performance.
In order to reduce this gap, other possible research direc-
tions could be explored by :(a) assessing the influence of
SinGAN’s receptive field (in our experiments the receptive
field was equal to 11) on the denoising performance, and
(b): manipulating the loss function by changing the recon-
struction loss term to a perceptual loss term and study the
perception-distortion tradeoff.
References
[1] Marc Lebrun. An analysis and implementation of the bm3d
imagedenoising method. Image Processing On Line, 2012. 1,
4, 5
[2] Tamar Rott Shaham Tali Dekel Tomer Michaeli. Singan:
Learning a generative model from a single natural image.
arXiv:1905.01164v2 [cs.CV], 2019. 1
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6. Appendix A
Figure 11: Qualitative comparison between our denoising approach(based on the clean training) with state-of the-art BM3D.
Appendix B
Figure 12: Noise is learned at finer scales: At coarser scales, many image details are still missing. As we move to finer scales,
more image details start to appear and then the noise pattern also emerges (in our case at scale 6). In total the architecture
has 12 scales.
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7. Appendix C
Figure 13: Qualitative comparison between our denoising approach(based on the noisy training) with state-of the-art BM3D.
Appendix D
Figure 14: Time that takes training a single scale over 2000 epochs. The time increases with the training image resolution,
using a fixed architecture across scales (so same number of trainable parameters)
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