Johns Hopkins University researchers have introduced a new tool called Progressively Deblurring Radiance Field (PDRF) that promises to enhance blurry images, making them clearer and sharper.
Led by post-doctoral fellow Cheng Peng from the Artificial Intelligence for Engineering and Medicine Lab, the team developed this method to significantly improve image clarity, surpassing previous techniques in both speed and quality.
"Oftentimes, images are blurry because autofocus doesn't work properly, or the camera or the subject moves. Our method allows you to transform those blurry images into something clear and three-dimensional," Cheng Peng said in a statement.
"Applications could include everything from virtual and augmented reality applications to 3D scanning for e-commerce to movie production to robotic navigation systems-not to mention just being used to sharpen and deblur personal photos and videos."
Pixel Perfect PDRF
According to the researchers, the conventional process of deblurring images typically involves two main steps: estimating the camera positions and reconstructing a detailed 3D model of the scene.
While effective to some extent, these methods often result in artifacts and incomplete reconstructions, particularly when dealing with low-quality input images.
However, according to the research team, PDRF stands out for its ability to produce clear and precise images even from blurry inputs.
Unlike traditional methods, PDRF incorporates a "Progressive Blur Estimation module" that not only detects and reduces blur but also sharpens the images before creating 3D reconstructions.
This self-supervised technique, based on neural networks, learns directly from the input images without the need for manually inputting training data.
As a result, the team noted that PDRF can handle various types of degradation, including camera shake, object movement, and out-of-focus scenarios, making it highly versatile and adaptable to real-world situations.
Detecting Skin Tumors
One notable application of PDRF is its collaboration with researchers in the Department of Dermatology at the Johns Hopkins School of Medicine to enhance the detection of skin tumors, particularly neurofibromatosis.
This innovative approach facilitates accurate analysis of tumor volume, positions, and quantity by creating precise 3D models, addressing the challenges posed by these tumors' soft and deformable nature.
According to Peng, this advancement holds significant promise in telemedicine or telehealth scenarios. In these scenarios, patients can capture affected areas using their cameras, improving diagnostic accuracy and patient care.
"Our ongoing project seeks to address this by creating precise 3D models, allowing for accurate analysis of tumor volume, positions, and quantity. This innovative approach holds particular promise in telemedicine or telehealth scenarios, where patients can use their own cameras to capture affected areas with this method being beneficial in improving diagnostic accuracy," said Peng.
What's next for PDRF?
Moreover, PDRF has garnered recognition and support from the Intelligence Advanced Research Projects Activity's (IARPA) Walk-Through Rendering of Images of Varying Altitude (WRIVA) program.
The objective of this program is to create software systems for modeling sites in situations where there is a scarcity of ground-level imagery accompanied by reliable metadata.
With contract support from IARPA, the researchers envision expanding the application of PDRF to large-scale reconstruction projects, paving the way for immersive mixed-reality experiences where individuals can explore distant lands and cities in 3D based on 2D images captured by amateur photographers.
The findings of the team were published in the Proceedings of the AAAI Conference on Artificial Intelligence.
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