Shayne  Bayer

Shayne Bayer


A Deep Journey into Super-resolution

A comprehensive analysis of Super-resolution Convolution Neural Networks to benchmark Single Image Super-resolution

#high-resolution #super-resolution #convolution-neural-net #generative-adversarial #deep-learning #programming

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A Deep Journey into Super-resolution
Shayne  Bayer

Shayne Bayer


A Deep Journey into Super-resolution

A comprehensive analysis of Super-resolution Convolution Neural Networks to benchmark Single Image Super-resolution

#high-resolution #super-resolution #convolution-neural-net #generative-adversarial #deep-learning #programming

Starting With Super Resolution

In this article, I will summarize my short experience with Super-Resolution task based on the PASCAL-VOC 2007 data set. During this session, I have tried several different models for super resolution of images from size of 72723 to 1441443 and to 2882883.

First thing needed for this task is getting the data set, I used the Kaggle

preloaded data set.

Because I trained different models and had a lot of data, for this entire work I used data generator for loading the images and for resizing them.

Data generator for loading images

import cv2
import os

image_dir = '../input/pascal-voc-2007/voctrainval_06-nov-2007/VOCdevkit/VOC2007/JPEGImages'
def load_images_iter(amount = 64,start_pos = 0, end_pos = 5011):
  ans = []
  all_im = os.listdir(image_dir)
  print (len(all_im))
  while (True):
    for idx,img_path in enumerate(all_im[start_pos:end_pos]):
      if (len(ans) !=0 and len(ans)%amount == 0):
        ret = ans
        ans = []
        yield ret

The sort function here isn’t mandatory. I used it because I use the generators not only for training and validating the model but also for looking at the model results. Therefore, I need to make sure I look at the same pictures all the time, So the sort function guarantees that.

I’ve created several different generators for the different uses. here is an example of a generator for the training set (the data was split that first 1000 pictures are for validation and the rest for training).

For resizing the images I used CV2 resize function.

Data Generator for Train

the generator returns a tuple, the first value is the train and the second value is another tuple of the output21 and output 2 expected results.

def train_generator_2(batch=64, tar1_size=144, tar2_size=288 , train_size=72):
  collect_train = []
  collect_target_1 = []
  collect_target_2 = []
  while True:
      file_gen = load_images_iter(amount=batch, start_pos = 
                          1000)#first 1000 images are for validation
      imgs = []
      while (True):
          imgs = next(file_gen)
        for idx,img in enumerate(imgs): 
          if (len(collect_train)!=0 and
                  len(collect_train)%batch == 0):
            ans_train = np.asarray(collect_train,dtype=np.float)
            ans_target_1 = 
            ans_target_2 = 
            collect_train = []
            collect_target_1 = []
            collect_target_2 = []
            yield (ans_train, (ans_target_1,ans_target_2))

As with any machine learning and deep learning jobs, I started with some data exploration.

We have 5011 different pictures with different sizes. The Pictures have no common motive between them, or any noticeable similarity . Here are some sample pictures In different sizes:

Image for post

For all the models in this assignment I used MSE as the loss function and PSNR as the metric.

The model’s loss was combined from the 2 outputs loss evenly. And each output had its own PSNR metric.

from keras import backend as K
import math

def PSNR(y_true, y_pred):
    max_pixel = 1.0
    return 10.0 * (1.0 / math.log(10)) * K.log((max_pixel ** 2) / (K.mean(K.square(y_pred - y_true))))

First Model

So the first model I’ll discuss is a basic model with 2 outputs for 2 different size images (1441443, 2882883).

Image for post

First Model Structure

From this structure, we can see that in order to get the required size we need to keep the original input height and width dimensions. So for the convolutional layers, I used padding.

inp = Input((72,72,3))
x = Conv2D(64,(3,3), activation = 'relu', padding = 'same')(inp)
x = Conv2D(64,(3,3), activation = 'relu', padding = 'same')(x)
x = UpSampling2D(size=(2,2))(x)
x = Conv2D(3,(1,1), activation = 'relu', padding = 'same')(x)

model1 = Model(inputs = inp ,outputs = x)

Image for post

First Model Loss

This model is pretty simple thus starts to converge pretty quickly as can be seen in the graph.

So In first sight, it seems we get pretty good results from the loss function but if we will take an actual look at the images they tell a different story.

In the picture below:

The 2 images on the right are the images downsized to sizes 144144, and 288288.

The 2 images on the left are the model predictions with respective sizes.

Image for post

Image for post

Downsized Images VS First Model Predictions

We can see here that although the loss is good we still actually get pretty bad results.

Image for post
First Model PSNR Graph

If we take a look at the PSNR graph we see it converges around the 21 which apparently isn’t such a good result. Also, we need to keep in mind that high PSNR doesn’t assure an image that looks good to the human eye, this is only an auxiliary metric to help us understand a little bit better how good or bad is the model.

Second Model

In this model, I tried to residual blocks instead of the convolutional layers.

Image for post

Model Structure

This model structure added more parameters to our model, so maybe it will help us improve the results we get.

Image for post

Second Model Loss & PSNR graphs

Here we can see that we get convergence after 2 epochs just as before, the loss is a tiny bit better and also the PSNR. But will this mean we also have better images?

#deep-learning #super-resolution #deep learning

Super Affiliate System Review - Recommended or Not?

Is it worth your money?

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John Crestanis’s extensive knowledge and skills in this industry set the Super Affiliate System far apart from competitor affiliate marketing systems. But is the Super Affiliate Commission System a genuine deal? Is it worth investing in? Today, in this Super Affiliate System review, we will take a look at what the system requires and decide whether it’s a real deal affiliate marketing enthusiasts should invest in.

What is the Super Affiliate System?

This is a complete training course that assists people in becoming successful affiliate marketers. The guide uses videos to lead you through the tools and processes you need to become a super affiliate marketer. The program creator has shared thriving, in-depth strategies to give you a life of freedom if you pay heed to them.

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Super Affiliate System Review: Pros and Cons

There are a few pros and cons that will enlighten beginner affiliates on whether to consider this system or not. Let’s have a look at them one by one:

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John Crestani, a 29-year-old expert in affiliate marketing from Santa Monica, California, is the program’s creator. The veteran left out of college and chose to earn money online since there are low job prospects. He failed several times, striving to make ends meet for quite some time until he successfully built a successful affiliate site dealing with health-related products.
He is currently a seven-figure person making more than $500 per month. His remarkable success in affiliate marketing has made him a featured in Yahoo Finance, Inc., Forbes, Business Insider, and Home Business magazine.

With the enormous success he has seen in affiliate marketing, John has designed an easy-to-follow guide to provide people with the skills to make money as an affiliate marketer. He has described all the strategies and tools he used to lead him to success.

Super Affiliate System Review: Does it Work?

The system accommodates affiliate marketers with in-depth details on how to develop successful affiliate networks. The Super Affiliate System review has a positive impact on different affiliate marketers who have tried it and noticed impressive results. But then, does it work?

The program doesn’t promise you overnight riches; it demands work and application to perform it. After finishing the Super Affiliate System online video training course, attaining success requires you to put John’s strategies into practice. A lot of commitment, hard work, and time are required in order to become a successful affiliate marketer.

How Does It Work?

As its name suggests, the Super Affiliate System is there to make you a super affiliate. John himself is an experienced affiliate, and he has accumulated all the necessary tools to achieve success in training others to become super affiliates. The Super Affiliate Network System members’ area has outlined everything that the veteran affiliate used to make millions as an affiliate.
The guide will help you set up campaigns, traffic resources, essential tools you need as an affiliate, and the veteran affiliate networks to achieve success.

Most amateur affiliates usually get frustrated as they might demand time to start making money. Those who succeed in getting little coins mainly do the following to earn;

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Affiliate marketing involves trading other people’s products and earning commissions from the sales you make. It’s an online business that can be done either with free or paid traffic. With the Super Affiliate System, one of the basic teachings you’ll get in the guide is how to make money by promoting the course itself using paid traffic Facebook ads.

What’s in the Super Affiliate System?

The system is amongst the most comprehensive affiliate marketing courses on the market. The Super Affiliate System comprises more than 50 hours of content that takes about six weeks to complete. The Super Affiliate System also includes several video lectures and tutorials alongside several questions and homework assignments to test its retention.

What Does the Super Affiliate Program Cover?

This program aims to provide affiliates with comprehensive ideas and tactics to become successful affiliate marketers. Therefore, their online video training course is comprehensive. Below are areas of information included within the modules;

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Advanced strategies

Besides the extensive information the creator has presented on these topics, he also went an extra mile to review the complete material and also guide marketers through the course.

Who is the Super Affiliate System for?

There are a number of digital products out there that provide solutions to techniques to earn money online. But not all options offer real value for what you want. John gives people a Super Affiliate System free webinar to allow them to learn what the system entails. It will help if you spare time to watch it, as it takes 90 minutes to get through. 
Below is a brief guide to who this system is for:

  1. It is for beginners who can equip themselves with appropriate affiliate marketing skills. People who are still employed and want to have an alternative earning scheme fit here.

  2. The system is also suitable for entrepreneurs who need to learn to earn money online, mainly using paid ads.

  3. The Super Affiliate System also suits anyone who is looking for another alternative stream of income.

Making money online has many advantages at large. You have the flexibility to work from any place, in the comfort of your home, with just an internet connection. Even though John has stated that there are no special skills needed to achieve success in affiliate marketing, there are little basics necessary to keep you on track. 
Having a proper mindset is also vital to attaining success in affiliate marketing. So, affiliates who believe in the system working for them need to be dedicated, focused, and committed. 
They incorporate;

Keep in mind that you have more than $895 in advertisements to get started. Furthermore, set aside a couple of dollars so that you keep on the right track.
There is also additional software you require to get started. It needs an extra of between $80 and $100 a month to get it.

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If you are interested in joining this big team, you have to get into the Super Affiliate System on the official website,, and get it from there. You have to pay their set fees to get their courses and other new materials within their learning scope.

Super Affiliate System Review: Is it Worth the Money?

It depends on an individual whether the system is worth it or not. The system is worth the money for serious people who want to go deep into an affiliate marketing career and have the time to put the Super Affiliate System strategies into practice. Super Affiliate System Review, Is it worth your money?
But people who also look forward to becoming rich overnight need to get off as this is not your way. Hard work and commitment are paramount to getting everything that works best for you.

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Marget D

Marget D


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Nat  Kutch

Nat Kutch


Single Image Super-Resolution Through Automated Texture Synthesis

In this article, the highlights of EnhanceNet paper for single image super-resolution and the performance of various versions of it are discussed. I would like to mainly emphasize its loss functions which are responsible for a perceptual quality close to the original high-resolution image.


In Single Image Super-Resolution, objective metric-based (like Mean Squared Error) CNN models might give good PSNR values but typically produce over-smoothed images, and thereby lack the ability to capture high-frequency features in an image. EnhanceNet is a Generative Adversarial Network which focusses on generating realistic textures along with higher perceptual quality rather than just improving on the PSNR values.

Network Architecture

Network Architecture of EnhanceNet for 4x upsampling

EnhanceNet to generate a 4x Super-resolved Image

  • EnhanceNet has a generic CNN architecture with a feed-forward fully convolutional network comprising of 10 residual blocks as they help in faster convergence of a model.
  • Instead of the convolution transpose layers, nearest neighbor upsampling followed by a convolution layer is used in the upsampling part of the network to avoid unnecessary artifacts.
  • Finally, a bicubic interpolation of the low-resolution image is added to the reconstructed output to avoid any color shifts and to ensure training stability.

#super-resolution #machine-learning #computer-vision #deep-learning #generative-adversarial #deep learning