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MarkTech: This Israel-Based Startup Uses Computer Vision to Attain the Highest Possible Image Quality on all Cameras


Originally published by MarkTechPost

Machine and computer vision are being used in more and more applications, driving sensor market growth to about 9% annually. When there are poor lighting conditions, such as low light, high dynamic range (such as when a person is silhouetted against a bright background), heavy rain, fog, increased motion, and sudden changes in intensity, like flashing headlights, devices that use hardware-based image signal processors produce subpar results., a Jerusalem, Israel-based startup, enables cameras to produce the highest possible image quality. The business has claimed to have created the first ISP (Image Signal Processor) that is entirely software-based and uses AI on the Edge to enhance the real-time image in challenging lighting, shadow, glare, and reflection situations. The company also adds that wherever there is a camera, is attaining market-leading image performance, giving consumer electronics, drones, robots, mobility, medical imaging, and other industries a competitive edge. has also claimed that it can reduce image noise and enhance real-time performance for various industry-standard image sensors, enhancing the functionality of cameras and machine vision applications.

The CEO and co-founder of the company Oren Debbi proclaim, “The market is experiencing dynamic growth for image sensors used for laptop cameras, medical endoscopy, safety reversing cameras, electronic doorbells, quality inspection on the assembly line, and hundreds of other applications. Our software-based ISP is continuously being updated with optimized algorithms to enable these sensors to create the highest possible quality images so people can stay connected, healthy, and safe.”

The company has raised $7 million to date, and the capital is being utilized towards R&D and business development that will further the company’s aim of making cutting-edge technology available to all cameras.

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