The integration of handcrafted models with neural networks presents exciting opportunities for mathematicians to explore and improve image denoising techniques.

Neural networks can struggle with images they haven't been trained on, highlighting the continued importance of handcrafted models in certain contexts.

Understanding the properties of handcrafted models can help inform the development of neural networks, particularly in terms of stability and performance.

The success of deep learning in AI has been driven by the ability to train models on large datasets, such as ImageNet, which has led to significant improvements in image recognition tasks. In the past few years, the error rate in image recognition has dropped dramatically, with some teams achieving as low as 3% error, which is considered superhuman performance.

The breakthrough moment in computer vision for deep learning was the 2012 AlexNet paper, which significantly outperformed previous algorithms in the ImageNet challenge.

Successful image denoising methods focus on preserving edges, which are crucial for maintaining the integrity of the image's features.

The introduction of large neural networks has allowed us to solve complex problems in vision, speech, and language, shifting focus from traditional handcrafted approaches to machine learning.

The last two years have been dominated by large AI models, which have proven to be popular and effective, but we are now entering an era focused on complex workflows around these models.

The success of deep learning is not just about algorithms; it's also about the availability of large datasets, like ImageNet, which have driven advancements in the field.