BLADE: Filter Learning for General Purpose Computational Photography

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 problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization

Possibility of Turbulence from a Post-Navier-Stokes Equation

We introduce corrections to the Navier-Stokes equation arising from the transitions between molecular states and the injection of external energy. In the simplest application of the proposed post Navier-Stokes equation, we find a multi-valued velocity field and the immediate possibility of velocity reversal, both features of turbulence

Chan-Vese Segmentation

While many segmentation methods rely heavily in some way on edge detection, the "Active Contours Without Edges" method by Chan and Vese ignores edges completely. Instead, the method optimally fits a two-phase piecewise constant model to the given image. The segmentation boundary is represented implicitly with a level set function, which allows the segmentation to handle topological changes more easily than explicit snake methods. This article describes the level set formulation of the Chan–Vese model and its numerical solution using a semi-implicit gradient descent. We also discuss the Chan–Sandberg–Vese method, a straightforward extension of Chan–Vese for vector-valued images

Gunturk-Altunbasak-Mersereau Alternating Projections Image Demosaicking

The problem of image demosaicking (or demosaicing) is where an image has been captured through a color filter array (CFA), and the goal is to estimate complete color information at every pixel. This IPOL article describes the image demosaicking method proposed by Gunturk, Altunbasak, and Mersereau in "Color Plane Interpolation Using Alternating Projections." Given an initial demosaicking, the method improves the result by alternatingly applying two different projections. One projection copies the green channel's wavelet detail coefficients to the red and blue channels while the other projection constrains the solution to agree with the observed data

Automatic Color Enhancement (ACE) and its Fast Implementation

Automatic Color Enhancement "ACE" is an effective method for color image enhancement introduced by Gatta, Rizzi, and Marini based on modeling several low level mechanisms of the human visual system. The direct computation of ACE on an NxN image costs O(N4) operations. This article describes two fast approximations of ACE. First, the algorithm of Bertalmío, Caselles, Provenzi, and Rizzi uses a polynomial approximation of the slope function to decomposes the main computation into convolutions, reducing the cost to O(N2 log N). Second, an algorithm based on interpolating intensity levels also reduces the main computation to convolutions. The use of ACE for image enhancement and color correction is demonstrated

Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman

Denoising is the problem of removing noise from an image. The most commonly studied case is with additive white Gaussian noise (AWGN), where the observed noisy image f is related to the underlying true image u by f=u+η and η is at each point in space independently and identically distributed as a zero-mean Gaussian random variable. Total variation (TV) regularization is a technique that was originally developed for AWGN image denoising by Rudin, Osher, and Fatemi. The TV regularization technique has since been applied to a multitude of other imaging problems, see for example Chan and Shen's book. We focus here on the split Bregman algorithm of Goldstein and Osher for TV-regularized denoising