Fast Image Super-Resolution via Local Adaptive Gradient Field Sharpening Transform.

Area of Science:

• Computer Vision
• Image Processing
• Signal Processing

Background:

• Single-image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs.
• Existing SISR methods often struggle with accurately recovering fine details and edge information.
• Traditional approaches require complex edge detection and gradient profile analysis.

Purpose of the Study:

• To propose an efficient and effective single-image super-resolution scheme.
• To introduce a novel gradient field sharpening transform for improved HR image reconstruction.
• To enhance image contrast and visual quality in super-resolved outputs.

Main Methods:

• A gradient field sharpening transform is introduced to convert blurry LR gradients to sharp HR gradients.
• The transform operates adaptively using local pixel neighborhoods, avoiding global edge analysis.
• Image gradient is adaptively scaled to preserve integral gradient field stability and image contrast.

Main Results:

• The proposed algorithm generates a more accurate gradient field compared to existing methods.
• Super-resolved images exhibit superior objective and visual quality.
• The gradient sharpening transform is computationally efficient, suitable for low-complexity applications.

Conclusions:

• The novel gradient sharpening transform offers a promising approach for single-image super-resolution.
• The method achieves high-quality results with improved accuracy and efficiency.
• This technique is well-suited for real-time and resource-constrained applications.