Wavelet-based background and noise subtraction for fluorescence microscopy images

Fluorescence microscopy images are inevitably contaminated by background intensity contributions. Fluorescence from out-of-focus planes and scattered light are important sources of slowly varying, low spatial frequency background, whereas background varying from pixel to pixel (high frequency noise) is introduced by the detection system. Here we present a powerful, easy-to-use software, wavelet-based background and noise subtraction (WBNS), which effectively removes both of these components. To assess its performance, we apply WBNS to synthetic images and compare the results quantitatively with the ground truth and with images processed by other background removal algorithms. We further evaluate WBNS on real images taken with a light-sheet microscope and a super-resolution stimulated emission depletion microscope. For both cases, we compare the WBNS algorithm with hardware-based background removal techniques and present a quantitative assessment of the results. WBNS shows an excellent performance in all these applications and significantly enhances the visual appearance of fluorescence images. Moreover, it may serve as a pre-processing step for further quantitative analysis

PMLR

Robustness to variations in lighting conditions is a key objective for any deep vision system. To this end, our paper extends the receptive field of convolutional neural networks with two residual components, ubiquitous in the visual processing system of vertebrates: On-center and off-center pathways, with an excitatory center and inhibitory surround; OOCS for short. The On-center pathway is excited by the presence of a light stimulus in its center, but not in its surround, whereas the Off-center pathway is excited by the absence of a light stimulus in its center, but not in its surround. We design OOCS pathways via a difference of Gaussians, with their variance computed analytically from the size of the receptive fields. OOCS pathways complement each other in their response to light stimuli, ensuring this way a strong edge-detection capability, and as a result an accurate and robust inference under challenging lighting conditions. We provide extensive empirical evidence showing that networks supplied with OOCS pathways gain accuracy and illumination-robustness from the novel edge representation, compared to other baselines

Computational model of dot-pattern selective cells

A computational model of a dot-pattern selective neuron is proposed. This type of neuron is found in the inferotemporal cortex of monkeys. It responds strongly to groups of dots and spots of light intensity variation but very weakly or not at all to single dots and spots that are not part of a pattern. This non-linear behaviour is quite different from the spatial frequency filtering behaviour exhibited by other neurons that react to spot-shaped stimuli, such as neurons with centre-surround receptive field profiles found in the lateral geniculate nuclei and layer 4Cβ of V1. It is implemented in the proposed computational model by using an AND-type non-linearity to combine the responses of centre-surround cells. The proposed model is capable of explaining the results of neurophysiological experiments as well as certain psychophysical observations.

Biol. Cybern. 83,313±325 �2000) Computational model of dot-pattern selective cells

Abstract. A computational model of a dot-pattern selective neuron is proposed. This type of neuron is found in the inferotemporal cortex of monkeys. It responds strongly to groups of dots and spots of light intensity variation but very weakly or not at all to single dots and spots that are not part of a pattern. This non-linear behaviour is quite di€erent from the spatial frequency ®ltering behaviour exhibited by other neurons that react to spot-shaped stimuli,such as neurons with centresurround receptive ®eld pro®les found in the lateral geniculate nuclei and layer 4Cb of V1. It is implemented in the proposed computational model by using an ANDtype non-linearity to combine the responses of centresurround cells. The proposed model is capable of explaining the results of neurophysiological experiments as well as certain psychophysical observations.