Guided Filter based Edge-preserving Image Non-blind Deconvolution
This work addresses image restoration for applications like photography or medical imaging, but it is incremental as it builds on existing deconvolution methods with a novel filter approach.
The paper tackles the problem of edge-preserving image deconvolution by proposing a new algorithm based on a guided filter, achieving competitive results in terms of ISNR and visual quality compared to existing techniques.
In this work, we propose a new approach for efficient edge-preserving image deconvolution. Our algorithm is based on a novel type of explicit image filter - guided filter. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter, but has better behaviors near edges. We propose an efficient iterative algorithm with the decouple of deblurring and denoising steps in the restoration process. In deblurring step, we proposed two cost function which could be computed with fast Fourier transform efficiently. The solution of the first one is used as the guidance image, and another solution will be filtered in next step. In the denoising step, the guided filter is used with the two obtained images for efficient edge-preserving filtering. Furthermore, we derive a simple and effective method to automatically adjust the regularization parameter at each iteration. We compare our deconvolution algorithm with many competitive deconvolution techniques in terms of ISNR and visual quality.