Dear Colleagues,

Blind image restoration aims to recover an image from its blurred observation without prior knowledge of the blur mathematical description.

While common imaging systems continue to gain higher quality and image resolution, sources of blur degradation such as motion, diffusing imaging mediums, de-focus, imperfect system components, and others still exist and may be even more significant with a higher imaging resolution. In most real-life blur causes, only a limited prior knowledge exists regarding blur characteristics, and thus, some kind of blind image restoration is required. Some of the methods approach this challenge by first estimating the blur, and then use it to restore the latent sharp image, while others perform both operations iteratively.

Recently, with the advance of powerful computing and machine learning via deep neural networks, learning-based methods have been developed, which, unlike most previous approaches, inherently do not necessarily require space-invariant and linear image blurring models. Although these new developments promise a more flexible and efficient blur removal, challenges in blind image restoration in various complicated scenarios are yet to be addressed.

Applications of blind image restoration cover a large variety of disciplines, including medical, industrial, and security.

This Special Issue wishes to gather diverse and complementary contributions that demonstrate novel developments, techniques, and applications that will further advance the field of blind image restoration.

Dr. Yitzhak Yitzhaky
Guest Editor

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• image degradations
• spatially-varying blur
• ill-posed problems
• blur estimation
• blind deconvolution
• inverse problems
• motion blur
• imaging through diffusive medium
• imaging through the atmosphere