BetterView's up-conversion technology utilizes Super-Resolution (SR) reconstruction that performs a fusion of low quality images into a higher quality result with improved optical resolution. This task encompasses scaling-up of the visual content by introducing true (optical) resolution enhancement.

Several low-resolution images of the same scene. Note they are slightly different from each other.

One HR image (with more pixels, better optical resolution, and less noise), obtained by fusing the previous images.

It has been known for the past 20 years that, in principle, one could take several low-quality images and fuse them into a single, higher-resolution outcome. This has been demonstrated by scientists, adopting various techniques and algorithms. This process became a hot field in image processing, with thousands of academic papers published during the past two decades on the problem and ways to handle it. The classical approach to fuse the low-quality images requires finding an exact correspondence between their pixels, a process known as "motion estimation".

BetterView technology is based on a recently developed and patent-pending novel family of SR algorithms, proposed by a world-leader in this field, Prof. Michael Elad (Technion – Israel Institute of Technology). Elad and his collaborator, Dr. Matan Protter devised the first method that overcomes the requirement for very accurate and explicit motion estimation in previous SR technologies.

Exact motion estimation has been a crucial stage in every earlier SR algorithm, considerably limiting the scenes that can be handled. The new family of SR techniques avoids the exact motion estimation and replaces it by a probabilistic estimate. This enables handling successfully general content scenes containing extremely complex motion patterns. The results are impressive, with no visual artifacts, and the process is completely robust.

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