A de facto standard in solving computer vision problems is to use a common
high-resolution camera and choose its placement on an agent (i.e., position and
orientation) based on human intuition. On the other hand, extremely simple and
well-designed visual sensors found throughout nature allow many organisms to
perform diverse, complex behaviors. In this work, motivated by these examples,
we raise the following questions: 1. How effective simple visual sensors are in
solving vision tasks? 2. What role does their design play in their
effectiveness? We explore simple sensors with resolutions as low as one-by-one
pixel, representing a single photoreceptor First, we demonstrate that just a
few photoreceptors can be enough to solve many tasks, such as visual navigation
and continuous control, reasonably well, with performance comparable to that of
a high-resolution camera. Second, we show that the design of these simple
visual sensors plays a crucial role in their ability to provide useful
information and successfully solve these tasks. To find a well-performing
design, we present a computational design optimization algorithm and evaluate
its effectiveness across different tasks and domains, showing promising
results. Finally, we perform a human survey to evaluate the effectiveness of
intuitive designs devised manually by humans, showing that the computationally
found design is among the best designs in most cases.

通过探索简单的视觉传感器在解决视觉任务中的有效性和设计的作用，我们展示了少数光感受器即可解决诸多任务，并通过计算设计优化算法找到了具有良好性能的设计。

用简单感光元件而非相机解决视觉任务

Solving Vision Tasks with Simple Photoreceptors Instead of Cameras

Photoreceptors in the retina are coupled by electrical synapses called "gap
junctions". It has long been established that gap junctions increase the
signal-to-noise ratio of photoreceptors. Inspired by electrically coupled
photoreceptors, we introduced a simple filter, the PR-filter, with only one
variable. On BSD68 dataset, PR-filter showed outstanding performance in SSIM
during blind denoising tasks. It also significantly improved the performance of
state-of-the-art convolutional neural network blind denosing on non-Gaussian
noise. The performance of keeping more details might be attributed to small
receptive field of the photoreceptors.

本研究利用视网膜中的光感受器之间通过电突触相互耦合的特性，设计了一种只有一个可变量的滤波器 PR-filter，能够有效的提高盲降噪任务中的 SSIM 性能，并且在非高斯噪声的情况下，极大的改善了现有卷积神经网络盲降噪模型的性能表现，其更好的保存细节信息可能归因于光感受器的小接收野。