Linear Filter Kernel Estimation Based on Digital Camera Sensor Noise

Abstract

We study linear filter kernel estimation from processed digital images under the assumption that the image's source camera is known. By leveraging easy-to-obtain camera-specific sensor noise fingerprints as a proxy, we have identified the linear crosscorrelation between a pre-computed camera fingerprint estimate and a noise residual extracted from the filtered query image as a viable domain to perform filter estimation. The result is a simple yet accurate filter kernel estimation technique that is relatively independent of image content and that does not rely on hand-crafted parameter settings. Experimental results obtained from both uncompressed and JPEG compressed images suggests performances on par with highly developed iterative constrained minimization techniques.