Abstract
Abstract. In these last years have been calculated several general algorithms to process the images acquired by mobile devices. Nevertheless, they don't always work at their full effectiveness due the numerous constraints and external issues. The latest generation smartphones, pushed by higher user expectations, have increasingly performing camera functions, in particular way, about the always increasing resolution, with a large number of pixels to process or two cameras for stereo view. Considering that pixels in an image sensor synthetizes the number of incoming photons, taking a photograph with a digital camera means to applying a low-pass filter to a scene where the tiny textures, such as characters that measure a few pixels, are observed blurry. This happens when using digital zoom or when the visibility is compromised or during the night artificial lights. For all these reasons the conventional image converter resolution algorithms (as bilinear interpolation algorithm) don’t work with the high-frequency information of a scene once lost. All these aspects are more relevant if we are taking photos to carry out a 3D scenario. Indeed, the 3D model will have an higher geometric accuracy if the image resolution will be higher. Super-Resolution algorithms (SRa) are classified into two categories: (1) approaches that reconstruct a high-resolution image from itself and (2) approaches that register multiple low-resolution images to interpolate sub-pixel information. In this paper we verify the geometric accuracy of a 3D model, when using the Morpho Super-Resolution™ algorithm, also in critical condition. This algorithm doesn’t require pixel shift, indeed, some cameras have a functionality named pixel shift, which captures multiple images while shifting the image sensor.
Highlights
The super-resolution (SR) of the images, that allows to carry out a high-resolution image from a single low resolution image, presents many solutions that can be associated to any given lowresolution pixel
The traditional methods include nearest-neighbor interpolation, linear, bilinear, bicubic interpolation, etc.; Nearest-neighbor Interpolation – The nearest-neighbor interpolation that is a simple and intuitive algorithm that selects the value of the nearest pixel for each position to be interpolated regardless of any other pixels; Bilinear Interpolation – The bilinear interpolation (BLI) that shows much better performance than nearest-neighbor interpolation while keeping a relatively fast speed and last but not least the Bicubic Interpolation – the bicubic interpolation (BCI) that performs cubic interpolation on each of the two axes compared to BLI, the BCI takes 4 × 4 pixels into account, and results in smoother results with fewer artifacts but much lower speed
The high resolution image was used as ground truth source and the low one was used for the processing in Super- resolution
Summary
The super-resolution (SR) of the images, that allows to carry out a high-resolution image from a single low resolution image, presents many solutions that can be associated to any given lowresolution pixel. Frequently, during the photogrammetric survey in particular light or geometric conditions or big distance from the object, we carry out images that give problems during the generation of the mesh of the 3D model and the RMSE final value is not acceptable (Inzerillo, 2020). Another typical sample where the images are at a low resolution is when we need to have the 3D model by ancient photos of a no longer existing building. This step is described by a 3×3 non-singular matrix H:
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