IEEE Trans Image Process - New learning based super-resolution: use of DWT and IGMRF prior.

Tópicos

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Resumo

In this paper, we propose a new learning-based approach for super-resolving an image captured at low spatial resolution. Given the low spatial resolution test image and a database consisting of low and high spatial resolution images, we obtain super-resolution for the test image. We first obtain an initial high-resolution (HR) estimate by learning the high-frequency details from the available database. A new discrete wavelet transform (DWT) based approach is proposed for learning that uses a set of low-resolution (LR) images and their corresponding HR versions. Since the super-resolution is an ill-posed problem, we obtain the final solution using a regularization framework. The LR image is modeled as the aliased and noisy version of the corresponding HR image, and the aliasing matrix entries are estimated using the test image and the initial HR estimate. The prior model for the super-resolved image is chosen as an Inhomogeneous Gaussian Markov random field (IGMRF) and the model parameters are estimated using the same initial HR estimate. A maximum a posteriori (MAP) estimation is used to arrive at the cost function which is minimized using a simple gradient descent approach. We demonstrate the effectiveness of the proposed approach by conducting the experiments on gray scale as well as on color images. The method is compared with the standard interpolation technique and also with existing learning-based approaches. The proposed approach can be used in applications such as wildlife sensor networks, remote surveillance where the memory, the transmission bandwidth, and the camera cost are the main constraints.

Resumo Limpo

paper propos new learningbas approach superresolv imag captur low spatial resolut given low spatial resolut test imag databas consist low high spatial resolut imag obtain superresolut test imag first obtain initi highresolut hr estim learn highfrequ detail avail databas new discret wavelet transform dwt base approach propos learn use set lowresolut lr imag correspond hr version sinc superresolut illpos problem obtain final solut use regular framework lr imag model alias noisi version correspond hr imag alias matrix entri estim use test imag initi hr estim prior model superresolv imag chosen inhomogen gaussian markov random field igmrf model paramet estim use initi hr estim maximum posteriori map estim use arriv cost function minim use simpl gradient descent approach demonstr effect propos approach conduct experi gray scale well color imag method compar standard interpol techniqu also exist learningbas approach propos approach can use applic wildlif sensor network remot surveil memori transmiss bandwidth camera cost main constraint

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