IEEE Trans Image Process - Image multidistortion estimation.

Tópicos

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Resumo

We present a method for estimating the amount of noise and blur in a distorted image. Our method is based on the multiscale structural similarity (MS-SSIM) framework that, although designed to measure image quality, is used to estimate the amount of blur and noise in a degraded image given a reference image. We show that there exists a bijective mapping between the 2-D noise/blur space and the 3-D MS-SSIM space, which allows to recover distortion parameters. That mapping allows to formulate the multidistortion-estimation problem as a classical optimization problem. Various search strategies such as Newton, simplex, NewUOA, and brute-force search are presented and compared. We also show that a bicubic patch can be used to approximate the bijective mapping between the noise/blur space and the 3-D MS-SSIM space. Interestingly, the use of such a patch reduces the processing time by a factor of 40 without significantly reducing precision. Based on quantitative results, we show that the amount of different types of blur and noise in a distorted image can be recovered with accuracy of roughly 2% and 8%, respectively. Our methods are compared with four state-of-the-art noise- and blur-estimation techniques.

Resumo Limpo

present method estim amount nois blur distort imag method base multiscal structur similar msssim framework although design measur imag qualiti use estim amount blur nois degrad imag given refer imag show exist biject map d noiseblur space d msssim space allow recov distort paramet map allow formul multidistortionestim problem classic optim problem various search strategi newton simplex newuoa bruteforc search present compar also show bicub patch can use approxim biject map noiseblur space d msssim space interest use patch reduc process time factor without signific reduc precis base quantit result show amount differ type blur nois distort imag can recov accuraci rough respect method compar four stateoftheart nois blurestim techniqu

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