Sobolev gradients and image interpolation

We present here a new image inpainting algorithm based on the Sobolev gradient method in conjunction with the Navier-Stokes model. The original model of Bertalmio et al is reformulated as a variational principle based on the minimization of a well chosen functional by a steepest descent method. This provides an alternative of the direct solving of a high-order partial differential equation and, consequently, allows to avoid complicated numerical schemes (min-mod limiters or anisotropic diffusion). We theoretically analyze our algorithm in an infinite dimensional setting using an evolution equation and obtain global existence and uniqueness results as well as the existence of an $\omega$-limit. Using a finite difference implementation, we demonstrate using various examples that the Sobolev gradient flow, due to its smoothing and preconditioning properties, is an effective tool for use in the image inpainting problem

Image interpolation using Shearlet based iterative refinement

This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three main steps: (a) obtaining an initial estimate of the high resolution image using linear methods like FIR filtering, (b) promoting sparsity in a selected dictionary through iterative thresholding, and (c) extracting high frequency information from the approximation to refine the initial estimate. For the sparse modeling, a shearlet dictionary is chosen to yield a multiscale directional representation. The proposed algorithm is compared to several state-of-the-art methods to assess its objective as well as subjective performance. Compared to the cubic spline interpolation method, an average PSNR gain of around 0.8 dB is observed over a dataset of 200 images

Adpative image interpolation

Simple interpolation techniques like nearest neighbor, bilinear, bicubic in the past had gained popularity due to their simplicity and low computational cost. But with the advent of high performing machines, demand for better interpolation methods at the expense of their computational complexity has arised. In this endeavor, myriads of interpolation methods have been introduced. Some of which are based on edge intensity, curvature profile of image, fuzzy logic. While others are optimized for the particular needs like resistance to outliers, performance in real time basis etc. An extensive list of interpolation methods exists in literature. We have reviewed an adaptive interpolation technique based on Newton forward dierence. This difference provides a measure of goodness for grouping of pixels around the target pixel for interpolation

Temporal Image Interpolation

Tato práce se zabývá problematikou interpolace sekvence obrázků mezi dvěma klíčovými snímky. Hlavním cílem je návrh a implementace aplikace, která provádí interpolaci pomocí odhadu optického toku Farnebäck metodou. Aplikace vypočítává snímky dvěma metodami, které používají obousměrnou interpolaci. První metoda vybírá obrazový bod s okolím a druhá metoda vybírá pouze bod a rozmazává jej do nového snímku. Testování proběhlo na datech zachycujících různé druhy pohybů. Pokud byl optický tok odhadnut správně, interpolace proběhla v pořádku, v opačném případě byly interpolované snímky nepřesné. Zejména se jednalo o klíčové snímky s malým gradientem či s neurčitým pohybem.This thesis deals with issues of image interpolation between two key frames. Main objectives of the work are design and implementation of application which interpolates images using optical flow estimation based on Farnebäck method. Application computes pictures by two methods, which use two-way interpolation. The first method selects a pixel with neighborhood and the second method selects only pixel and blurs it into a new frame. Testing was carried out on data describing the different types of movements. If the estimation of optical flow was correct, interpolation was successful, otherwise the interpolated pictures were inaccurate. Especially it was the key frames with a small gradient or with an indeterminate movement.

Deep Semantic Image Interpolation

Image datasets often live on a continuum: Images from an outdoor scene vary from day to night, across different weather conditions, and over the course of seasons. Faces age and exhibit different expressions. We consider the problem of taking individual images from these datasets and explicitly manipulating those images to change where they lie on the continuum. We focus on a version of this problem that requires as little input as possible, and we build off of previous work using CNN features to construct an intermediate image manifold on which to manipulate the images. We also investigate a novel way of reconstructing images from their CNN features using alpha compositions of the input images. These technique produce convincing semantic interpolations of images and timelapse video from a variety of sources

Fast artifacts-free image interpolation

In this paper we describe a novel general purpose image interpolation method based on the combination of two different procedures. First, an adaptive algorithm is applied interpolating locally pixel values along the direction where second order image derivative is lower. Then interpolated values are modified using an iterative refinement minimizing differences in second order image derivatives, maximizing second order derivative values and smoothing isolevel curves. The first algorithm itself provides edge preserving images that are measurably better than those obtained with similarly fast methods presented in the literature. The full method provides interpolated images with a ”natural ” appearance that do not present the artifacts affecting linear and nonlinear methods. Objective and subjective tests on a wide series of natural images clearly show the advantages of the proposed technique over existing approaches.