Deep Learning for Image Super Resolution

September 12, 2022

Deep learning approaches have been relatively very successful in addressing problems of varying scales. Deep learning has demonstrated its capability and produced astounding outcomes in the realm of image and video super-resolution as well. 

In this blog, we will go over what image super-resolution is and various other aspects revolving around it.

What is Image Resolution?

The resolution of an image is the number of pixels displayed per square inch (PPI) of a digital image. The level of detail in an image is described by its resolution; a higher resolution indicates more image detail. 

In digital imaging, the resolution is frequently expressed as a pixel count. A pixel (short for picture element) is a single point or small square in a graphic image recorded in a rectangular grid. It is the tiniest component of a digital image. The greater the number of pixels employed to represent an image, the more closely the outcome might match the analogue original.

Figure: Image Resolution

Image Super-Resolution 

The technique of recovering high-resolution (HR) images from low-resolution (LR) photos is known as image super-resolution (SR). It is a significant class of image processing algorithms in computer vision and image processing, with several real-world applications including medical imaging, satellite imaging, surveillance and security, and astronomical imaging. 

Deep learning-based Super Resolution models have been intensively researched in recent years as deep learning techniques have advanced, and they frequently achieve state-of-the-art performance on several SR benchmarks. Deep learning methods ranging from the early Convolutional Neural Networks (CNN)-based method to recent promising Generative Adversarial Nets-based SR approaches have been used to address SR tasks.

Why Image Super Resolution?

Uses of Image Super Resolution

Image Super-Resolution helps in increasing the resolution of an image from low-resolution (LR) to high-resolution (HR) which is highly demanded in many different companies across the industries. 

It is commonly employed in the following applications: 

Surveillance: The detection, identification, and recognition of faces in low-resolution images received from security cameras. 

Figure: Super-Resolution in IR Surveillance Videos

Media: Super-resolution can be used to reduce server costs, as media can be sent at a lower resolution and upscaled on the fly.

Figure: Super Resolution in Broadcast Media

Medical: Obtaining high-resolution MRI pictures can be difficult due to scan time, spatial coverage, and signal-to-noise ratio considerations (SNR). Super-resolution helps resolve this by generating high-resolution MRI from otherwise low-resolution MRI images. 

Figure: (a) Image before ISR, (b) Image after ISR

Mathematical Representation for ISR

The following formula can be used to model low-resolution images from high-resolution photographs: 

D is the degradation function,  Iy is the high-resolution image, Ix is the low-resolution image, and σ is the noise. Only the high-resolution image and the equivalent low-resolution image are provided; the degradation parameters D and σ are unknown. The neural network's job is to identify the inverse function of deterioration using just the HR and LR image data.


Before we can grasp the rest of the theory behind super-resolution, we must first understand upsampling.

Upsampling is the process of increasing the spatial resolution of images or simply the number of pixel rows/columns or both in the image.

Learning Strategies for Super Resolution

Because image super-resolution is an ill-posed problem, the essential issue is how to execute upsampling (i.e., generating high-resolution output from low-resolution input). Based on the upsampling processes(Interpolation and Learning based ) used and their positions in the model, there are primarily three model frameworks.

  1. Pre-upsampling 

The LR input image is first upsampled to suit the dimensions of the desired HR output in this family of algorithms. The upscaled LR image is then processed using a Deep Learning model. VDSR (Very Deep Super Resolution) is an early SR attempt that employs the Pre Upsampling approach. The VDSR network is based on an extremely deep (20 weight layers) convolutional network inspired by VGG networks.

Deep networks typically converge slowly when learning rates are modest. However, increasing convergence and learning rates may result in ballooning gradients. To solve these challenges, residual learning and gradient clipping have been utilised in VDSR. Furthermore, VDSR solves multi-scaled SR problems with a single network.

  1. Post-upsampling

Increasing the resolution of the LR pictures before the image enhancement phase (in Pre Upsampling approaches) increases the computational cost. This is especially problematic for convolutional networks, whose processing speed is directly proportional to the resolution of the input image. Second, traditional interpolation methods, such as bicubic interpolation, do not provide new information to answer the ill-posed reconstruction problem. 

Thus, in the Post Upsampling class of SR approaches, the LR image is first enhanced using a deep model, then upscaled to fulfil the HR image dimension constraints using classic techniques such as bicubic interpolation.

  1. Progressive-upsampling

Both pre and post-up sampling procedures are useful. However, for instances where LR images must be upscaled by huge factors (say, 8x), the results will be unsatisfactory regardless of whether the upsampling is performed before or after passing through the deep SR network. 

In such circumstances, rather than upscaling by 8x in one shot, it makes more sense to gradually upscale the LR image until it meets the spatial dimension parameters of the HR output. Progressive Upsampling methods are those that employ this learning strategy.

The LapSRN, or Laplacian Pyramid Super-Resolution Network architecture, is one such model that progressively reconstructs the sub-band residuals of HR pictures. Sub-band residuals are the discrepancies between the upsampled image and the ground truth HR image at each network level.

Popular Architecture

Several Deep Learning-based models have been presented to handle the SR problem over the years, some of which were innovative at the time and served as stepping stones for future study in SR technology. Let us now look at some of the most prevalent SR architectures.


SRCNN (Super-Resolution Convolutional Neural Network) is a simple CNN architecture with three layers: one for patch extraction, one for non-linear mapping, and one for reconstruction. The patch extraction layer extracts dense patches from the input and uses convolutional filters to represent them. The non-linear mapping layer is made up of 11 convolutional filters that are used to modify the number of channels and introduce non-linearity. The final reconstruction layer, as you might expect, reconstructs the high-resolution image.


To generate visually appealing images, SRGAN employs a GAN-based architecture. It exploits a multi-task loss to improve the findings and uses the SRResnet network architecture as a backend. 

The loss is composed of three terms: 

  1. Pixel similarity is captured by MSE loss. 
  2. A deep network is used to capture high-level information via perceptual similarity loss. 
  3. The discriminator's adversarial loss

Although the produced findings had lower PSNR (peak signal-to-noise ratio) values, the model achieved more MOS, implying a higher perceptual quality in the data.


ESPCN or Efficient Sub-Pixel CNN is made up of feature extraction convolutional layers followed by sub-pixel convolution for upsampling.

Sub-pixel convolution works by translating depth to space. In a high-resolution image, pixels from numerous channels in a low-resolution image are rearranged to form a single channel. To illustrate, a 54-pixel input image can be used to rearrange the pixels in the final four channels into a single channel, yielding a 10X10 HR image.


Image Super-Resolution, which seeks to improve the resolution of a degraded/noisy image, is a critical Computer Vision task because of its numerous applications in health, astronomy, and security. Deep Learning has significantly contributed to the advancement of SR technology to its current status. 

While we have already produced fantastic outcomes using SR technology, the majority of them have been obtained by fully Supervised Learning, which includes training a deep model with a massive amount of labeled data. Large amounts of data may not be readily available, particularly in applications such as medical imaging, where only qualified doctors may annotate the data. As a result, contemporary SR research has focused on decreasing, if not completely eliminating, supervision from SR tasks.

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October 6, 2022

Comparison between Cloud-Based and On Premises GPUs

Cloud GPUs vs On Premises GPUs

Cloud GPUs are typically more powerful than on-premises GPU instances. The cost of renting a cloud GPU is generally lower than the cost of purchasing an on-premise GPU. 

Cloud platforms offer fast access to high performance compute and deep learning algorithms, which makes it simpler to start using machine learning models and get early insights into your data. 

Cloud GPUs are better for machine learning because they have lower latency, which is important because the time it takes a neural network to learn from data affects its accuracy. Furthermore, cloud GPUs allow users to take advantage of large-scale training datasets without having to build and maintain their own infrastructure.

On Premises GPUs are better for machine learning if you need high performance or require access to cutting-edge technologies not available in the public cloud. For example, on-premises hardware can be used for deep learning applications that require high memory bandwidth and low latency.

Cloud GPUs: Cloud GPUs are remote data centers where you can rent unused GPU resources. This allows you to run your models on a massive scale, without having to install and manage a local machine learning cluster.

Lower TCO: Cloud GPUs require no upfront investment, making them ideal for companies that are looking to reduce their overall capital expenses. Furthermore, the cost of maintenance and upgrades is also low since it takes place in the cloud rather than on-premises.

Scalability & Flexibility: With cloud-based GPU resources, businesses can scale up or down as needed without any penalty. This ensures that they have the resources they need when demand spikes but also saves them money when there is little or no demand for those resources at all times.

Enhanced Capacity Planning Capabilities: Cloud GPU platforms allow businesses to better plan for future demands by providing estimates of how much processing power will be required in the next 12 months and beyond based on past data points such as workloads run and successes achieved with similar models/algorithms etc... 

Security & Compliance : Since cloud GPUs reside in a remote datacenter separate from your business' core systems, you are ensured peace of mind when it comes to security and compliance matters (eigenvector scanning / firewalls / SELinux etc...) 

Reduced Total Cost Of Ownership (TCO) over time due to pay-as-you-go pricing model which allows you only spend what you actually use vs traditional software licensing models where significant upfront investments are made.

Cloud GPUs: Cloud GPUs offer significant performance benefits over on-premises GPUs. They are accessible from anywhere, and you don't need to own or manage the hardware. This makes them a great choice for data scientists who work with multiple data sets across different platforms.

Numerous Platforms Available for Use: The wide variety of available platforms (Windows, Linux) means that you can run your models using the most popular machine learning libraries and frameworks across different platforms without having to worry about compatibility issues between them.

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October 4, 2022

Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

As the rupee is inching closer to 82 per dollar, the strong dollar has directly impacted the costs of cloud services for Indian businesses. The high cost of storage and computing power, along with bandwidth charges from overseas vendors, has led to a huge increase in the effective rate of these services. This is especially true for startups and SMEs that rely on cloud computing to store and process user data. With the strong dollar continuing to impact the cost of cloud services, it is essential for Indian companies to evaluate their options and adopt local alternatives wherever possible. This blog post will discuss how the strong dollar impacts cloud costs, as well as potential Indian alternatives you can explore in response to this global economic trend. 

What is a Strong Dollar?

A strong US dollar($) is a term used to describe a situation where a US’s currency has appreciated in value compared to other major currencies. This can be due to a variety of factors, including interest rate changes, a country’s current account deficit, and investor sentiment. When a currency appreciates, it means that it is worth more. A strong dollar makes imports more expensive, while making exports cheaper. Strong dollars have been a growing trend in the past couple of years. As the US Federal Reserve continues to hike interest rates, the dollar strengthens further. The rising value of the dollar means that the cost of cloud services, especially from hyperscalers based in the US, will rise as well. 

Increase in Cloud Costs Due to Strong Dollar

Cloud services are essential for modern businesses, as they provide easy access to software, storage, and computing resources. Cloud services are delivered over the internet and are typically charged on a per-use basis. This makes them incredibly convenient for businesses, as they can pay for only the resources they actually use. Cloud computing allows businesses to scale their resources up or down, depending on their current business needs. This makes it suitable for startups, where demand is uncertain, or large enterprises with global operations. Cloud computing is also inherently scalable and allows businesses to quickly react to changing business needs. Cloud computing is a very competitive industry and providers offer attractive prices to attract customers. However, these prices have been impacted by the strong dollar. The dollar has strengthened by 15-20% against the Indian rupee in the last few years. As a result, the costs of services such as storage and bandwidth have increased for Indian companies. Vendors charge their Indian customers in Indian rupees, taking into account the exchange rate. This has resulted in a significant rise in the costs of these services for Indian companies.

Why are Cloud Services Becoming More Expensive?

Cloud services are priced in US dollars. When the dollar is strong, the effective price of services will be higher in Indian rupees, as the cost is not re-adjusted. There are a couple of reasons for this price discrepancy. First, Indian customers will have to pay the same prices as American customers, despite a weaker Indian rupee. Second, vendors have to ensure that they make a profit.

Possible Indian Alternatives to Cloud Services

If you're looking for a cost-effective substitute for services provided by the U.S.-based suppliers, consider E2E Cloud, an Indian cloud service provider. When it comes to cloud services, E2E Cloud provides everything that startups and SMEs could possibly need.

The table below lists some of these services and compares their cost against their US equivalents. 

According to the data in the table above, Indian E2E Cloud Services are much cheaper than their American equivalents. The difference in price between some of these options is substantial. When compared to the prices charged by suppliers in the United States, E2E Cloud's bandwidth costs are surprisingly low. Although not all E2E Cloud services will be noticeably less expensive. Using Indian services, however, has an additional, crucial perk: data sovereignty.


The price of cloud services will rise as the US Dollar appreciates. Indian businesses will need to find ways to counteract the strong dollar's impact on their bottom lines. To do this, one must use E2E Cloud. The availability of E2E Cloud services in INR currency is a bonus on top of the already substantial cost savings. An effective protection against the negative effects of a strong dollar.

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September 28, 2022

Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.


In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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