A new CFA interpolation framework

A new CFA interpolation framework

The paper introduces a new color filter array (CFA) interpolation method for digital still cameras. The proposed interpolation scheme is able to (i) overcome the hardware limitations of existing CFA based image acquisition solutions, and (ii) restore color images with excellent visual quality. The scheme employs an adaptive edge-sensing mechanism which operates along the vertical, horizontal an...

CFA Interpolation Detection

To capture a still photograph, digital cameras have to employ a technology converting incoming photons into digital signals. The image sensor used for this is most often a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) chip, however regardless of the technology, the effect is the same: light strikes an array of photosensors, where energy proportional to that of ...

Identifying Digital Cameras Using CFA Interpolation

Improvements on Source Camera-model Identification Based on Cfa Interpolation

The idea of using traces of interpolation algorithms, deployed by a digital camera, as an identifier in the source camera-model identification problem has been initially studied in [2]. In this work, we improve our previous approach by incorporating methods to better detect the interpolation artifacts in smooth image parts. To identify the source cameramodel of a digital image, new features tha...

A New Shearlet Framework for Image Denoising

Traditional noise removal methods like Non-Local Means create spurious boundaries inside regular zones. Visushrink removes too many coefficients and yields recovered images that are overly smoothed. In Bayesshrink method, sharp features are preserved. However, PSNR (Peak Signal-to-Noise Ratio) is considerably low. BLS-GSM generates some discontinuous information during the course of denoising a...