Super resolution Image

Super resolution with Deep Learning: SRGAN

The Super Resolution GAN (SRGAN) is a deep learning algorithm whose objective is to improve the quality of an image. It belongs to the family of GANs (Generative Adversarial Networks).

In this article, we will first discuss the background of image super-resolution and the different deep learning algorithms used for image processing. We will then see more precisely what a GAN is and how it works, before diving into the specific architecture of SRGAN. Finally, we will explore its applications, limitations, and future directions.

- Why image super-resolution matters -

In today’s digital world, we are surrounded by images—from social media photos and surveillance videos to medical scans and satellite imagery. However, many of these images suffer from poor resolution due to compression, noise, or hardware limitations. Super-resolution aims to reconstruct high-quality images from low-resolution inputs.

Traditional approaches to image enhancement included interpolation methods like bilinear or bicubic interpolation. While fast, these methods often produce blurry and unrealistic results because they cannot restore lost fine details. Deep learning, particularly CNNs and GANs, revolutionized the field by learning from vast datasets to hallucinate missing details, producing sharper and more natural-looking images.

- GANs and their functioning -

A GAN is an algorithm capable of generating or transforming images. It was introduced by Ian Goodfellow in 2014 and has since transformed AI research.

GANs consist of two main components:

These two networks are trained simultaneously in a minimax game. The generator improves by learning to fool the discriminator, while the discriminator improves by learning to detect fakes. Over time, the generator becomes highly skilled at producing realistic images.

There are many GAN variants, each optimized for a specific use case. For instance, StyleGAN by NVIDIA generates hyper-realistic human faces. Conditional GANs (cGANs) generate images based on labels or attributes. CycleGANs perform style transfer, converting paintings to photos or horses to zebras. SRGAN is the variant specialized for super-resolution.

- SRGAN: How the algorithm works -

Introduced in 2017 by Ledig et al., SRGAN was the first framework capable of generating photo-realistic images for 4x upscaling tasks. Its innovation lies in combining adversarial learning with perceptual loss functions derived from pretrained networks.

The architecture includes three main components:

1. Generator

The generator is a deep residual network (ResNet) with multiple residual blocks. Each block includes convolutional layers, batch normalization, and Parametric ReLU activations. The goal is to upsample low-resolution inputs (e.g., 64x64) into high-resolution outputs (e.g., 256x256) while adding realistic textures and edges.

2. Discriminator

The discriminator is a CNN classifier trained to distinguish between real high-resolution images and generated ones. It provides adversarial feedback to push the generator toward realism.

3. VGG19 network

SRGAN introduces a perceptual loss based on VGG19, a pretrained CNN on ImageNet. Instead of comparing images pixel-by-pixel (which often yields blurry results), SRGAN compares feature maps extracted by VGG19. This ensures the generated image not only matches in structure but also in high-level perceptual quality.

- Loss functions in SRGAN -

The effectiveness of SRGAN comes from its loss design:

- Training SRGAN -

Training SRGAN requires:

  1. Dataset preparation: Pairs of low- and high-resolution images (e.g., DIV2K, ImageNet subsets).
  2. Pretraining: The generator is often pretrained with MSE loss for stability.
  3. Adversarial training: The generator and discriminator are trained alternately.
  4. Evaluation: Metrics include PSNR, SSIM, and human perceptual studies.

- Applications of SRGAN -

- Limitations and improvements -

Although powerful, SRGAN has some limitations:

These issues led to improvements such as:

- Code example (PyTorch) -

import torch
import torch.nn as nn

# Residual block for SRGAN
class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels, channels, 3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(channels)
        self.prelu = nn.PReLU()
        self.conv2 = nn.Conv2d(channels, channels, 3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(channels)

    def forward(self, x):
        residual = self.conv1(x)
        residual = self.bn1(residual)
        residual = self.prelu(residual)
        residual = self.conv2(residual)
        residual = self.bn2(residual)
        return x + residual

This snippet shows how residual blocks are implemented in the generator. Stacking multiple such blocks allows SRGAN to model complex image textures.

- The future of SRGAN and super-resolution -

The field is rapidly evolving:

Ultimately, SRGAN and its successors are paving the way for a future where any low-quality image can be transformed into a sharp, realistic representation—breaking the barriers of hardware limitations and unlocking new possibilities in science, media, and communication.