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.