Super resolution

Increase resolution of images
with deep neural networks

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What is super-resolution reconstruction? (SRR)

The aim of the super-resolution reconstruction techniques is to improve the quality and increase the resolution of images (upscale images) while restoring as many details as possible from the source image. Additional goals include:

  • Preserving sharp edges

  • Limiting the number of unwanted image distortions after the transformations

  • Producing a visually appealing image

The SRR developed by KP Labs is based on deep neural networks and advanced statistical methods offering top reconstruction performance. What’s more, application of evolutionary algorithms allowed us to improve these methods further.

SRR applications

Satellite imaging

Imaging from nano-satellite constellations or other low to medium resolution imagery

Machine vision systems

Production monitoring and inspection and control systems

Pattern recognition

Bar codes (1D) or QR codes (2D) and OCR applications

SRR comparison

The SRR methods can use a single, low-resolution image (single-image SRR) or a number of images showing the same scene or object (multiple-image SRR).

Single-image SRRMultiple-image SRR
Focus onImprovement of image appearance Fusion of images and extraction of new information
Number of input imagesOne Even only a few up to tens and more
Overall quality improvementLittleSignificant

Implementation of SRR projects within the FP Space consortium

January 2017

Launch of the first SRR project (SISPARE) commissioned by the European Space Agency (ESA)

June 2018

The first stage of SRR project successfully concluded with a positive ESA assessment

December 2018

Launch of second project (SuperDeep) focusing on the use the machine learning process to improve the quality of satellite images


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