Temporal structure in spiking patterns of ganglion cells defines perceptual thresholds in rodents with subretinal prosthesis.

Subretinal prostheses are designed to restore sight in patients blinded by retinal degeneration using electrical stimulation of the inner retinal neurons. To relate retinal output to perception, we studied behavioral thresholds in blind rats with photovoltaic subretinal prostheses stimulated by full-field pulsed illumination at 20 Hz, and measured retinal ganglion cell (RGC) responses to similar stimuli ex-vivo. Behaviorally, rats exhibited startling response to changes in brightness, with an average contrast threshold of 12%, which could not be explained by changes in the average RGC spiking rate. However, RGCs exhibited millisecond-scale variations in spike timing, even when the average rate did not change significantly. At 12% temporal contrast, changes in firing patterns of prosthetic response were as significant as with 2.3% contrast steps in visible light stimulation of healthy retinas. This suggests that millisecond-scale changes in spiking patterns define perceptual thresholds of prosthetic vision. Response to the last pulse in the stimulation burst lasted longer than the steady-state response during the burst. This may be interpreted as an excitatory OFF response to prosthetic stimulation, and can explain behavioral response to decrease in illumination. Contrast enhancement of images prior to delivery to subretinal prosthesis can partially compensate for reduced contrast sensitivity of prosthetic vision

Learning a self-supervised tone mapping operator via feature contrast masking loss

High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can achieve remarkable performance in this task when compared to traditional methods, however, the quality of the results of these learning-based methods is limited by the training data. Most existing works use as training set a curated selection of best-performing results from existing traditional tone mapping operators (often guided by a quality metric), therefore, the quality of newly generated results is fundamentally limited by the performance of such operators. This quality might be even further limited by the pool of HDR content that is used for training. In this work we propose a learning-based self-supervised tone mapping operator that is trained at test time specifically for each HDR image and does not need any data labeling. The key novelty of our approach is a carefully designed loss function built upon fundamental knowledge on contrast perception that allows for directly comparing the content in the HDR and tone mapped images. We achieve this goal by reformulating classic VGG feature maps into feature contrast maps that normalize local feature differences by their average magnitude in a local neighborhood, allowing our loss to account for contrast masking effects. We perform extensive ablation studies and exploration of parameters and demonstrate that our solution outperforms existing approaches with a single set of fixed parameters, as confirmed by both objective and subjective metrics

Mini Kirsch Edge Detection and Its Sharpening Effect

In computer vision, edge detection is a crucial step in identifying the objects’ boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true non-edge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods.  The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy