03 / Single-image super-resolution
Image Resolution Enhancer using ESRGAN
A deep-learning pipeline that reconstructs sharp, high-resolution images from blurry or pixelated inputs while preserving believable texture.
The challenge
Problem & approach
Traditional interpolation enlarges pixels without recovering missing detail. This system uses an ESRGAN generator with Residual-in-Residual Dense Blocks and a relativistic discriminator to restore edges and natural texture, while tiled inference prevents memory failures on very large images.
VISUAL RESULT
Low resolution to restored detail
Generated demonstration example. Replace with a captured model output when final benchmark samples are available.
Natural texture
VGG-based perceptual loss avoids the overly smooth finish produced by pixel loss alone.
Memory-aware inference
Seamless tiling supports ultra-high-resolution outputs on consumer hardware.
System architecture
Prepare
Create paired low- and high-resolution samples with consistent crops and augmentation.
Generate
Pass the input through RRDB layers without batch normalization to reconstruct fine detail.
Optimize
Combine perceptual, adversarial, and content losses for fidelity and realistic texture.
Enhance
Run tiled inference and blend boundaries to process large images within available memory.
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