How do you compare and contrast different image deblurring models and frameworks?

2 Types of image deblurring models

There are two main types of image deblurring models: blind and non-blind. Blind models do not assume any prior knowledge about the blur kernel, which is the function that describes how the image was blurred. Non-blind models, on the other hand, require some information about the blur kernel, such as its size, shape, or direction. Blind models are more general and flexible, but also more difficult and computationally expensive. Non-blind models are more efficient and stable, but also more limited and sensitive to errors.

4 How to compare image deblurring models and frameworks

To compare different image deblurring models and frameworks, you need to consider several factors, such as the type and level of blur, the quality and diversity of the data, the speed and complexity of the computation, and the performance and robustness of the results. You can use various metrics to measure these factors, such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), perceptual quality index (PQI), inference time, memory usage, and generalization ability. You can also use visual inspection and subjective evaluation to assess the aesthetic and semantic aspects of the images.

5 How to choose the best image deblurring model and framework

There is no one-size-fits-all solution for image deblurring, as different models and frameworks have different strengths and weaknesses, and different scenarios have different requirements and preferences. Therefore, you need to choose the best image deblurring model and framework based on your specific goals and constraints. For example, if you want to deblur a single image with unknown blur kernel, you might want to use a blind deep learning model, such as a GAN. If you want to deblur a sequence of images with known blur kernel, you might want to use a non-blind conventional model, such as a deconvolution algorithm.