Sungjoon Choi
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Polyblur: Removing mild blur by polynomial reblurring
IEEE Transactions on Computational Imaging (2021)
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We present a highly efficient blind image restoration method to remove mild blur in natural images. Contrary to the mainstream, we focus on removing slight blur that is often present damaging image quality and commonly generated by small out-of-focus, lens blur or slight camera motion. The proposed algorithm first estimates image blur and then compensates for it by combining multiple applications of the estimated blur in a principle-based way. In this sense, we present a novel procedure to design the approximate inverse of a filter and make only use of re-applications of the filter itself. To estimate image blur in natural images we introduce a simple yet robust algorithm based on empirical observations about the distribution of the gradient in sharp images. Our experiments show that, in the context of mild blur, the proposed method outperforms traditional and modern blind deconvolution methods and runs in a fraction of time. We finally show that the method can be used to blindly correct blur before applying an out-of-the-shelf deep super-resolution model leading to superior results than other highly complex and computational demanding methods. The proposed method can estimate and remove mild blur on a 12Mp image on a modern mobile phone device in a fraction of a second.
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BLADE: Filter Learning for General Purpose Image Processing
John Isidoro
Frank Ong
International Conference on Computational Photography (2018)
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The Rapid and Accurate Image Super Resolution (RAISR)
method of Romano, Isidoro, and Milanfar is a computationally efficient image
upscaling method using a trained set of filters. We describe a generalization of
RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This
approach is a trainable edge-adaptive filtering framework that is general, simple,
computationally efficient, and useful for a wide range of image processing
problems. We show applications to denoising, compression artifact removal,
demosaicing, and approximation of anisotropic diffusion equations.
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Fast, Trainable, Multiscale Denoising
John Isidoro
IEEE International Conference on Image Processing (ICIP) (2018) (to appear)
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Denoising is a fundamental imaging application. Versatile but fast filtering has been demanded for mobile camera systems. We present an approach to multiscale filtering which allows real-time applications on low-powered devices. The key idea is to learn a set of kernels that upscales, filters, and blends patches of different scales guided by local structure analysis. This approach is trainable so that learned filters are capable of treating diverse noise patterns and artifacts. Experimental results show that the presented approach produces comparable results to state-of-the-art algorithms while processing time is orders of magnitude faster.
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