Pic courtesy : https://uwaterloo.ca/vision-image-processing-lab/research-demos/image-denoising

One of the fundamental challenges in the field of image processing and computer vision is image de-noising, where the underlying goal is to estimate the original image by suppressing noise from a noise-contaminated version of the image. Image noise may be caused by different intrinsic (i.e., sensor) and extrinsic (i.e., environment) conditions which are often not possible to avoid in practical situations.

Image de-noising plays an important role in computer vision tasks like image restoration, image segmentation, and even classification problems, where obtaining the original image content is crucial for strong performance.

In this blog, we will try to build…


Computer Vision

The Monk Object Detection Library is a low code library to perform object detection very easily and efficiently.

Source: Unsplash

About the project:

Brackish water creatures as fish, crabs, prawns are consumed by people all over the world. Fishermen fishing on these creatures tend to over-fish creating concern over marine biologists and scientists. As many marine creatures are becoming endangered.But what if we had a detector, that could detect these creatures. That would definitely reduce the risk of over-fishing, and help these species from becoming endangered.

Using Monk Object Detection, we can build this very easily. So let’s start making it!!

About the Data-set:

For training any model, the foremost thing required is data. The data set used for this purpose is titled “The Brackish Dataset”…


This classifier can classify aerial images, specifically between images captured by drones or images taken from helicopters/airplanes. Seven classes corresponding to coarse ground occupancy labels have been provided : Orchard, Vineyard, Urban, Forest , Water, Crop and Land.

Aerial image captured by drone

The classifier can be used to actively study and survey ground patches as what percentage of land is agricultural land, or what percentage has settlements in it, and also help in topological research.

About the dataset:

For this project, I have used the HistAerial image dataset, which contains grayscale images representing 9 square kilometer areas (~ 6k per 6k pixels per image). …


Computer Vision, Deep Learning

The Monk AI library is a low code library, for Computer Vision, that supports Mxnet-Gluon, Pytorch, and Keras backend. This is an amazing library that allows us to solve CV problems, easily.

The sign Languages for the alphabets

About the project:

This project is to classify between the sign languages, corresponding to the alphabets, and thereby allow us to interpret the information a person with speech-impairment is trying to tell.

This project is very beneficial and is easy to make as well using the MonkAI Library. …


Deep Learning, Programming

In this article, we will build a web-app trained on the SRCNN model.

The super-resolution web-app

On giving an image as input, it reconstructs a higher resolution image of the same. I made this app, as my pilot task for Tessellate coding. The task included finding a suitable model, making the inference algorithm, wrapping it in a REST API, and finally dockerizing the application.

For the task, I used Keras with a Tensorflow backend and Flask. This blog is about the same challenges I faced in the task, and how to overcome them when you are making your project.

Step 1: Finding a suitable model

For the model, I researched a bit on the topic of the super-resolution of the image, and…


Generative Adversarial Networks or GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio’s lab. Since then, GANs have exploded in popularity. Here are a few examples to check out:

The idea behind GANs is that one has two networks, a generator 𝐺, and a discriminator 𝐷, competing against each other. The generator makes “fake” data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it’s received is real or fake.

  • The generator is…


Deep learning and Machine Learning,are the two most hot topics in field of technology,but it involves a lot of matrix math, and it’s important for us to understand the basics before diving into building our own neural networks.

NumPy is a Python Library, to work easily and efficiently with matrices in Python.It is one such library: that provides fast alternatives to math operations in Python and is designed to work efficiently with groups of numbers — like matrices.

Using NumPy in Python:

One can install numpy in Python, using pip. Type the command:pip install numpy,to include numpy library.

To include it in the code…


Ever felt that you want to use API search results in your website or web app, yet thought that its to cumbersome to use it.

Using Jquery, you can do it in a snap.Irrespective of the framework you use, React, Angular or pure Vanilla.

Make the basic structure :

You just need this much.

Here I have included the CDN link for jquery,you can also download and include it.

Getting the API key:

You can use any JSON based API, as per your requirement.Any JSON based response giving API will work fine with this.

Here I am airvisual API, as an example.

Visit:https://www.airvisual.com/air-pollution-data-api, to read documentation and get an…


Making an image editor,does not require knowledge of heavy libraries or pixel manipulation.Its very easy to do,using Caman.js and simple CSS and HTML.

This effects can easily be created with a few lines of code.

Getting Caman.js library:

Run these commands in your Terminal.

npm (NodeJS): npm install caman

bower (Browser):bower install caman

Or get cdn link from: https://cdnjs.com/libraries/camanjs

Setting the UI:

You can design the UI as per your choice,using Bootstrap, SASS, CSS or anything you like!!!

Aanisha Bhattacharyya

Deep Learning enthusiast | Adobe WIT Scholar'21

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