Person: Chakrabarti, Ayan
Loading...
Email Address
AA Acceptance Date
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Chakrabarti
First Name
Ayan
Name
Chakrabarti, Ayan
3 results
Search Results
Now showing 1 - 3 of 3
Publication Depth and Deblurring from a Spectrally-varying Depth-of-Field(Springer Verlag, 2012) Chakrabarti, Ayan; Zickler, ToddWe propose modifying the aperture of a conventional color camera so that the effective aperture size for one color channel is smaller than that for the other two. This produces an image where different color channels have different depths-of-field, and from this we can computationally recover scene depth, reconstruct an all-focus image and achieve synthetic re-focusing, all from a single shot. These capabilities are enabled by a spatio-spectral image model that encodes the statistical relationship between gradient profiles across color channels. This approach substantially improves depth accuracy over alternative single-shot coded-aperture designs, and since it avoids introducing additional spatial distortions and is light efficient, it allows high-quality deblurring and lower exposure times. We demonstrate these benefits with comparisons on synthetic data, as well as results on images captured with a prototype lens.Publication Fast Deconvolution with Color Constraints on Gradients(2012) Chakrabarti, Ayan; Zickler, ToddIn this report, we describe a fast deconvolution approach for color images that combines a sparse regularization cost on the magnitudes of gradients with constraints on their direction in color space. We form these color constraints in a way that allows retaining the computationally-efficient optimization strategy introduced in recent deconvolution methods based on half-quadratic splitting. The proposed algorithm is capable of handling a different blur kernel in each color channel, and is used for per-layer deconvolution in our paper: “Depth and Deblurring from a Spectrally-varying Depth-of-Field." A MATLAB implementation of this method is available at http://vision.seas.harvard.edu/ccap, and takes roughly 20 seconds to deconvolve a three-channel 1544 × 1028 color image, on a Linux-based Intel I-3 2.1GHz machine.Publication Computational Color Constancy with Spatial Correlations(2010) Chakrabarti, Ayan; Hirakawa, Keigo; Zickler, ToddThe color of a scene recorded by a trichromatic sensor varies with the spectral distribution of the illuminant. For recognition and many other applications, we seek to process these measurements to obtain a color representation that is unaffected by illumination changes. Achieving such color constancy is an ill-posed problem because both the spectral distribution of the illuminant and the scene reflectance are unknown. For the most part, methods have approached this problem by leveraging the statistics of individual pixel measurements, independent from their spatial contexts. In this work, we show that the strong spatial correlations that exist between measurements at neighboring image points encode useful information about the illuminant and should not be ignored. We develop a method to encode these correlations in a statistical model and exploit them for color constancy. The method is computationally efficient, allows for the incorporation of prior information about natural illuminants, and performs well when evaluated on a large database of natural images.