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Super-resolution reconstruction
Super-resolution reconstruction (SRR) is a technique that enhances image quality and resolution while preserving details, sharp edges, and minimizing distortions. KP Labs' SRR, based on deep neural networks and advanced statistical methods, achieves top reconstruction performance, further optimized using evolutionary algorithms.
What is super-resolution reconstruction?
The aim of the super-resolution reconstruction (SRR) 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.
KP Labs has acquired an advanced SRR technology based on deep neural networks and statistical methods, ensuring high-quality image reconstruction. After the acquisition, the team further optimized the technology by applying evolutionary algorithms to improve its efficiency and performance.
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
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).
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CEO, XYZ Corp
Jane Smith
CTO, ABC Inc
Mike Johnson
COO, DEF Corp
Sarah Williams
CFO, GHI Inc
Super-resolution reconstruction
The aim of the super-resolution reconstruction (SRR) 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.
Φsat-2
- The Φsat-2 mission demonstrates advanced AI capabilities in Earth observation through a 6U satellite equipped with a multispectral instrument, launched in August 2024.
- KP Labs developed a CNN-based algorithm for automatic cloud detection, optimizing data transmission by filtering out cloud-covered images before downlink.
- The project implemented 8-bit quantization to ensure algorithm efficiency within CubeSat hardware limitations while maintaining effective real-time image processing capabilities.
ORCHIDE (Orchestration of Reliable Computing on Heterogeneous Infrastructures at the Edge)
- The ORCHIDE project addresses satellite communication limitations by developing an edge computing ecosystem for autonomous data processing and AI integration in space.
- The system implements a software framework for in-orbit reprogramming and deploys machine learning models directly on satellites, enabling autonomous operations without constant ground contact.
- The solution focuses on real-time decision-making and decentralized computing architecture, supporting extended missions through enhanced system autonomy and resource optimization.
Space-Based Data Centers
- The project addresses the challenge of efficiently handling growing satellite data volumes, as transferring all data to Earth for processing leads to delays and high bandwidth costs.
- KP Labs developed autonomous, in-orbit data processing systems for the Space-Based Data Centers project, designing distributed architectures for large-scale data storage and processing directly in space.
- The project validated machine learning models for space-based systems, explored innovative uses in space exploration, and enabled real-time processing of Earth observation data, reducing latency and bandwidth requirements.