IEEE Trans Image Process - Multifocusing and depth estimation using a color shift model-based computational camera.

Tópicos

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Resumo

This paper presents a novel approach to depth estimation using a multiple color-filter aperture (MCA) camera and its application to multifocusing. An image acquired by the MCA camera contains spatially varying misalignment among RGB color channels, where the direction and length of the misalignment is a function of the distance of an object from the plane of focus. Therefore, if the misalignment is estimated from the MCA output image, multifocusing and depth estimation become possible using a set of image processing algorithms. We first segment the image into multiple clusters having approximately uniform misalignment using a color-based region classification method, and then find a rectangular region that encloses each cluster. For each of the rectangular regions in the RGB color channels, color shifting vectors are estimated using a phase correlation method. After the set of three clusters are aligned in the opposite direction of the estimated color shifting vectors, the aligned clusters are fused to produce an approximately in-focus image. Because of the finite size of the color-filter apertures, the fused image still contains a certain amount of spatially varying out-of-focus blur, which is removed by using a truncated constrained least-squares filter followed by a spatially adaptive artifacts removing filter. Experimental results show that the MCA-based multifocusing method significantly enhances the visual quality of an image containing multiple objects of different distances, and can be fully or partially incorporated into multifocusing or extended depth of field systems. The MCA camera also realizes single camera-based depth estimation, where the displacement between multiple apertures plays a role of the baseline of a stereo vision system. Experimental results show that the estimated depth is accurate enough to perform a variety of vision-based tasks, such as image understanding, description, and robot vision.

Resumo Limpo

paper present novel approach depth estim use multipl colorfilt apertur mca camera applic multifocus imag acquir mca camera contain spatial vari misalign among rgb color channel direct length misalign function distanc object plane focus therefor misalign estim mca output imag multifocus depth estim becom possibl use set imag process algorithm first segment imag multipl cluster approxim uniform misalign use colorbas region classif method find rectangular region enclos cluster rectangular region rgb color channel color shift vector estim use phase correl method set three cluster align opposit direct estim color shift vector align cluster fuse produc approxim infocus imag finit size colorfilt apertur fuse imag still contain certain amount spatial vari outoffocus blur remov use truncat constrain leastsquar filter follow spatial adapt artifact remov filter experiment result show mcabas multifocus method signific enhanc visual qualiti imag contain multipl object differ distanc can fulli partial incorpor multifocus extend depth field system mca camera also realiz singl camerabas depth estim displac multipl apertur play role baselin stereo vision system experiment result show estim depth accur enough perform varieti visionbas task imag understand descript robot vision

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