Plug-and-play approach to class-adapted blind image deblurring

Most of the existing single-image blind deblurring methods are tailored for natural images. However, in many important applications (e.g., document analysis, forensics), the image being recovered belongs to a specific class (e.g., text, faces, fingerprints) or contains two or more classes. To deal with these images, we propose a class-adapted blind deblurring framework, based on the plug-and-play scheme, which allows forward models of imaging systems to be combined with state-of-the-art denoisers. We consider three patch-based denoisers, two suitable for images that belong to a specific class and a general purpose one. Additionally, for images with two or more classes, we propose two approaches: a direct one, and one that uses a patch classification step before denoising. The proposed deblurring framework includes two priors on the blurring filter: a sparsity-inducing prior, suitable for motion blur and a weak prior, for a variety of filters. The results show the state-of-the-art performance of the proposed framework when applied to images that belong to a specific class (text, face, fingerprints), or contain two classes (text and face). For images with two classes, we show that the deblurring performance is improved by using the classification step. For these images, we choose to test one instance of the proposed framework suitable for text and faces, which is a natural test ground for the proposed framework. With the proper (dictionary and/or classifier) learning procedure, the framework can be adapted to other problems. For text images, we show that, in most cases, the proposed deblurring framework improves OCR accuracy.