Deepening CNNs for Image Super-Resolution: A Comprehensive Review

In this article, we dive into the world of image super-resolution (SR), exploring how deep learning techniques can help enhance the quality of low-resolution (LR) images. SR is crucial in various applications, including surveillance, healthcare, and entertainment.
Let’s start with the basics: traditional SR methods rely on interpolating missing pixels from a small set of available high-resolution (HR) images. However, these approaches often result in blurry or distorted images. Deep learning models have revolutionized SR by learning the mapping between LR and HR images directly from data.
Convolutional Neural Networks (CNNs) are the workhorse of deep learning-based SR models. These networks learn features from LR images, upsample them to HR resolution, and voila! – you get a higher-quality image. Dong et al.’s work in 1994 introduced the first deep learning model into the SR domain, setting the stage for subsequent advancements.
FSRCNN, proposed later by the same authors, aimed to minimize model complexity while maintaining performance. This approach focused on improving computational efficiency without sacrificing image quality. CNN-based models with attention mechanisms and deeper networks have also gained popularity in SR applications where computing power is limited.
Generative Adversarial Networks (GANs) are another class of models that have shown remarkable performance in SR tasks. These models learn the nonlinear mapping between LR and HR images by optimizing a generative loss function. GANs can recover more image edge detail texture information than CNN-based models, but they also carry the risk of model collapse during training.
Recently, transformer models have emerged as a new paradigm in computer vision. These models are based on the self-attention mechanism and capture long-range dependencies excellently, making them popular for SR tasks. CNNs combined with transformer decoders offer a balance between local feature extraction and global information reconstruction, leading to superior performance.
In summary, deep learning techniques have revolutionized image SR by learning the mapping between LR and HR images directly from data. Various models have been proposed to improve performance, and GANs have shown remarkable ability to recover image details. Transformer models have also emerged as a new paradigm in SR, offering an excellent balance between local feature extraction and global information reconstruction.