Long Short-Term Memory Networks for Image Enhancement and Restoration

This paper provides a systematic review on the use of Long Short-Term Memory (LSTM) networks in post-processing of advanced images to enhance and restore, the aim of which is to deliver Photoshop-like effects. It tracks the historical shift in the paradigm of traditional image-processing approaches toward deep-learning approaches, highlighting the specific advantages of LSTMs in handling sequential data and addressing the vanishing-gradient problem inherent to traditional Recurrent Neural Networks. The review outlines the original LSTM structure and its further development into hybrid versions, known as Convolutional LSTMs (ConvLSTMs), with respect to spatial and temporal reasoning (Courtney, 2019). It provides an example of its use in various applications, including super-resolution, denoising, deblurring, and inpainting. Possible methods, such as stateful training protocols, attention potential, and quantitative image-quality metrics (PSNR, SSIM, FID), are discussed. Lastly, the paper concludes by synthesising empirical performance references, computational bottlenecks, and data-dependency hurdles, and singles out future research paths, thus clarifying the challenges by which LSTM-based strategies will help transform the sphere of digital image manipulation.