Image Processing

Image Processing Applications

Image Processing is seen and used in different fields of human activities. For example, in medicine Image Processing is applied for both diagnostic and therapeutic purposes. Processing of images obtained from satellites is widespread task in the space sector. Also, Image Processing is used in the entertainment, e.g. shooting movies.
Finally, it is well-known that Image Processing is needed in almost all computer vision applications.

Simmakers Image Processing Offerings

Since our team has been specializing in the development and implementation of complex Image Processing algorithms for more than 10 years, we are ready to solve the most challenging task for you.

Our offerings include:

  • Linear and Non-Linear Filtering
  • Filtering in the Spatial and Frequency Domains
  • Wavelet Analysis with Applications to Image Processing
  • Image Denoising
  • Image Deblurring
  • Image Recovery
  • Image Compression
  • Morphological Image Processing
  • Image Segmentation
  • Image Recognition
  • Image Super Resolution
  • Image Pansharpening


Simmakers ltd collaborates with the world’s leading research centers – the Massachusetts Institute of Technology (MIT) and the University of California, Los Angeles (UCLA).

Organizations Simmakers partners with

Why Clients Choose Simmakers

With Simmakers, you get a competent solution created by highly qualified specialists in  data mining, image processing, software engineering and applied mathematics.

Tasks, performed by Simmakers specialists:

We have several advantages that allow us to solve problems successfully:

  • Partnership with NVIDIA. Being partners with NVIDIA , the world’s largest producer of graphics cards and GPUs, we apply the corporation’s latest achievements in the development of IT-solutions in computer graphics, data visualization and parallelization of computations.
  • Extensive experience. Cooperating with customers from North America, Western Europe, Russia for more than a decade, our specialists have completed more than 30 complex projects on data visualization and computer simulation of physical and technological processes for various industries, including construction engineering, oil and gas extraction, metallurgy, film industry, healthcare, arts, etc.
  • Profound technological expertise. Simmakers specialists have won high recognition and international awards in various fields and are professionals in applied mathematics, IT and software development. We actively collaborate with the leading international research and development centers, such as the Massachusetts Institute of Technology (MIT), the University of California (UCLA) and the Skolkovo Institute of Science and Technology.
  • Custom-tailored service. In the development of IT solutions, we make the demands and needs of each customer as our highest priority. This approach allows us to develop trusting and mutually beneficial relations with customers resulting in beneficial effect on the efficiency of project implementation.
Case studies

Listed below are some of our featured projects.

Deformable Registration
Deformable Registration
Backend for OSL Functions
Acceleration of Image Processing
Software-Studio for Image Processing
Emotion Recognizer

If you are looking for a company with a strong background in both low-level and high-level programming, then you have come to the right place. With expertise in highly specialized technologies and specific programming languages, our IT engineers will ensure you successfully implement your Image Processing project objectives.

Take advantage of our best practices in the following fields of study:

Programming languages:


  • C CUDA
  • C OpenCL
  • C# .NET
  • C++ 03/11/14
  • Matlab
  • Java
  • Python
  • OpenGL modern
  • CUDA  (including PTX)
  • DirectX
  • OpenCL
  • Processing (Java)
  • Qt 3D
  • WPF (.NET C#)
  • OpenGL ES (mobile)

Image processing libraries:

Graphics applications used (plugins development):

  • OpenCV
  • Boost GIL
  • Magick++
  • CImg
  • AForge.Net
  • ImageJ
  • Scikit-image
  • Blender
  • Maya
  • VRay
  • 3DSMax
  • Aurora
  • Foundry Nuke
  • Cinema 4D
Frequently Asked Questions (FAQ)

Q: What is Image Processing?
A: Any form of signal processing for which the input data is an image or a set of images is called by Image Processing.

Q: What is Image Denoising?
A: Image Denoising is the process of removing noise from an image. There are well-known denoising techniques, such as total variation denoising, neighborhood filters, non-local means and so on.

Q: What is Image Deblurring?
A: Usually taking photos, we want the recorded image to be a faithful representation of the scene that we see – but every image is more or less blurry. There are different causes of blurry photos. So, for examples, an image that is out of focus will appear blurry. Also blur appears when the subject moves while the shutter is open, or the camera moves while the shutter is open. Image Deblurring is the process of removing blur from an image. Thus, Image Deblurring is fundamental in making pictures sharp and useful. Many deblurring methods were designed. For example, Wiener filters, Richardson–Lucy deconvolution, Tikhonov regularization and so on.

Q: What is Image Compression?
A: Image Compression is an application of data compression that transforms and encodes the original image with few bits. The purpose of image compression is to minimize the redundancy of the image and to store or transmit data in an efficient form. Image compression may be lossy or lossless. Lossless compression is preferred for medical imaging, technical drawings and so. Lossy compression methods, especially when used at low bit rates, introduce compression artifacts. Lossy methods are especially convenient for natural images such as photographs. In applications minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate.

Q: What is Morphological Image Processing?
A: Morphological Image Processing is a collection of non-linear operations related to the shape or morphology of features in an image. Morphological operations rely only on the relative ordering of pixel values, not on their numerical values, and therefore are especially suited to the processing of binary images.

Q: What is Image Recognition?
A: By Image Recognition we call the process of identifying and detecting an object or a feature in a digital image or video. This concept is used in many applications like systems for factory automation, toll booth monitoring, and security surveillance. Typical image recognition algorithms include:

  • Optical character recognition, i.e. the electronic conversion of images of typewritten or printed text into machine-encoded text;
  • Pattern and gradient matching;
  • Face recognition, i.e. automatic identifying or verifying a person from a digital image or a video frame from a video source;
  • License plate matching;
  • Scene change detection.

Q: What is Image Segmentation?
A: Image Segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. In other words, Image Segmentation is the task of finding groups of pixels that “go together”. In statistics, this problem is known as cluster analysis and is a widely studied area with hundreds of different algorithms.

Q: What is Image Pansharpened?
A: Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. This technique often is used in satellite image processing to increase image quality. In this case, pansharpening produces a high-resolution color image from three, four or more low-resolution multispectral satellite bands plus a corresponding high-resolution panchromatic bands.

Q: What is Image Super Resolution?
A: Superresolution is a technique that enhance the resolution of an imaging system beyond their sensor and optics limits. Super-resolution can combine set of low resolution images to obtain a single image of higher resolution. Also there is known algorighms which produce high resolution image using single image.