Image Enhancement
Image Enhancement
Image enhancement techniques improve the visual quality and interpretability of satellite images. This includes:
Contrast adjustment
Brightness correction
Atmospheric correction
Geometric correction
Sharpening filters (deblurring)
Noise reduction
Challenges in Image Enhancements
Dealing with diverse types of distortions and artifacts
Preserving fine details and textures during restoration
Handling large data volumes efficiently
Adapting to different sensors and imaging conditions
Applications of Enhanced Images
Environmental monitoring
Urban planning
Agriculture management
Disaster response
Climate change studies
I2R works with clients to optimize the image enhancement methodology and implementation dealing with unique conditions
Enhancements Details:
Geometric Correction: Geometric correction adjusts for distortions caused by sensor orientation, Earth's curvature, and terrain variations. This ensures accurate spatial alignment of image features.
Radiometric Calibration: Radiometric calibration converts raw sensor data to meaningful physical units, accounting for atmospheric effects and sensor characteristics. This is essential for quantitative analysis of satellite imagery. Radiometric calibration also includes dark frame, flat field and linearity corrections that enhance data through corrections.
Denoising: Advanced denoising algorithms, such as bilateral filters and sparse 3D transform-domain collaborative filtering, can effectively remove noise while preserving image details.
Deblurring: Deblurring techniques address motion blur and optical defocus in images. Methods like adaptive sparse domain selection can restore sharpness.
These restoration techniques can significantly enhance images, providing clearer, more accurate, and more informative data for various observation applications.
Denoising
I2R develops image enhancement algorithms to reduce the noise and remove aperture shadows or other optical system/focal plane array artifacts within an image.
Denoising is the process of reducing the signal noise in an image without removing signal information. Noise in an image appears as speckles scattered throughout the scene and can be correlated or random, lowering image quality. Denoising employs detailed processes to reduce noise without creating blur or decolorization. Increasing the Signal-to-Noise Ratio (SNR) through denoising creates higher quality images useful in photography and geospatial work.
Noise reduction improves image quality and SNR
Various denoising filters can be applied, cleaning the image without reducing information
Sharpness and illumination can be preserved with proper settings for the filters
Additionally, algorithms can be developed to remove other image artifacts that may occur, including aperture shadows or focal plane array artifacts.
I2R provides:
Noise reduction with custom filters.
Images with less noise while preserving color and sharpness.
Algorithms to integrate into existing image processing software to improve noise reduction and other image attributes.