This paper reviews the state-of-the-art in denoising methods for biological microscopy
images and introduces a new and original sparsity-based algorithm. The proposed method …

The recent advances in hardware and imaging systems made the digital cameras
ubiquitous. Although the development of hardware has steadily improved the quality of …

Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …

Photon-limited imaging arises when the number of photons collected by a sensor array is
small relative to the number of detector elements. Photon limitations are an important …

Digital images are matrices of equally spaced pixels, each containing a photon count. This
photon count is a stochastic process due to the quantum nature of light. It follows that all …

Fluorescence microscopy imaging speed is fundamentally limited by the measurement
signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate …

The easy-to-compute Anscombe transform offers a conversion of a Poisson random variable
into a variance stabilized Gaussian one, thus becoming handy in various Poisson-noisy …

Objective Dynamic positron emission tomography (PET), which reveals information about
both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative …

Many tasks in computer vision require to match image parts. While higher-level methods
consider image features such as edges or robust descriptors, low-level approaches (so …

A novel image sequence denoising algorithm is presented. The proposed approach takes
advantage of the self-similarity and redundancy of adjacent frames. The algorithm is inspired …