Tuesday, March 16, 2010

Paper watch: Image sensors and noise

(Apologies to those behind pay walls for the papers)

Two related papers on image sensors and noise.

* "Correction of dark current in consumer cameras"
A study of dark current in digital imagers in digital single-lens reflex (DSLR) and compact consumer-grade digital cameras is presented. Dark current is shown to vary with temperature, exposure time, and ISO setting. Further, dark current is shown to increase in successive images during a series of images. DSLR and compact consumer cameras are often designed such that they are contained within a densely packed camera body, and therefore the digital imagers within the camera frame are prone to heat generated by the sensor as well as nearby elements within the camera body. It is the scope of this work to characterize the dark current in such cameras and to show that the dark current, in part due to heat generated by the camera itself, can be corrected by using hot pixels on the imager. This method generates computed dark frames based on the dark current indicator value of the hottest pixels on the chip. We compare this method to standard methods of dark current correction.

* "Is Denoising Dead?"
Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable. A pertinent question then to ask is whether there is a theoretical limit to denoising performance and, more importantly, are we there yet? As camera manufacturers continue to pack increasing numbers of pixels per unit area, an increase in noise sensitivity manifests itself in the form of a noisier image. We study the performance bounds for the image denoising problem. Our work in this paper estimates a lower bound on the mean squared error of the denoised result and compares the performance of current state-of-the-art denoising methods with this bound. We show that despite the phenomenal recent progress in the quality of denoising algorithms, some room for improvement still remains for a wide class of general images, and at certain signal-to-noise levels. Therefore, image denoising is not dead—yet.

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