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Computer Vision Workflow Enhancement – Image Restoration with ProHawk
Overview
Introduction
Business challenges
Solution overview Partner overview Architecture Sizing and scaling guidance Use cases Conclusions and References

Business challenges

Home > Workload Solutions > Computer Vision > White Papers > Computer Vision Workflow Enhancement – Image Restoration with ProHawk > Business challenges

Business challenges

  • Organizations implementing CV and VMS solutions generally face one (or more) of the following business challenges concerning managing image and video quality:

    Noise and Distortion
    Poor quality images or video may contain noise, artifacts, or distortion due to factors such as low lighting conditions, sensor limitations, compression, or transmission errors. Any of these issues can negatively impact the performance of CV algorithms, leading to decreased reliability and inaccurate analytics.
    Resolution and Clarity
    Low-resolution or blurry images and video frames can make it difficult for CV systems to extract granular details and accurately detect objects or patterns. This challenge is particularly relevant regarding surveillance footage, remote sensing imagery, or images captured from devices with limited capabilities.
    Illumination Variations
    CV algorithms often struggle with images or videos that are captured with variations in lighting conditions. Underexposure/overexposure or even the presence of shadows can affect the overall video quality compounding the challenges of extracting meaningful information from video data.
    Compression Artifacts
    If video data requires compression for storage or transmission, the resulting data may be susceptible to instances of compression artifacts such as blockiness, blurring, ringing, or color shifts. These artifacts may make it more difficult for CV algorithms to accurately interpret the visual data and may produce unreliable results.
    Occlusions and Partial Visibility
    In real-world scenarios, objects or regions of interest may be hidden or partially obscured, impacting the visibility and quality of the captured video. This can hinder the accurate detection, recognition, or tracking of objects, thus limiting the effectiveness of CV and AI systems.
    Real-Time Processing
    CV applications may require real-time or near-real-time processing of video streams. However, high-quality video processing in real time is inherently resource-demanding and compute intensive. Understanding the tradeoff and striking a balance between acceptable video quality and real-time performance is a constant challenge in maintaining CV solutions.
    Variability Across Devices
    The quality of images and video captured by various devices can differ significantly, making it challenging for CV solutions to consistently perform across a wide range of devices. Robust CV systems need to handle the variability in hardware and device capabilities.

    This solution primarily focuses on assisting with:

    • Noise and Distortion
    • Resolution and Clarity
    • Illumination Variations
    • Occlusions and Partial Visibility
    • Real-Time Processing