25,597 research outputs found
A smartphone-based chip-scale microscope using ambient illumination
Portable chip-scale microscopy devices can potentially address various imaging needs in mobile healthcare and environmental monitoring. Here, we demonstrate the adaptation of a smartphone's camera to function as a compact lensless microscope. Unlike other chip-scale microscopy schemes, this method uses ambient illumination as its light source and does not require the incorporation of a dedicated light source. The method is based on the shadow imaging technique where the sample is placed on the surface of the image sensor, which captures direct shadow images under illumination. To improve the image resolution beyond the pixel size, we perform pixel super-resolution reconstruction with multiple images at different angles of illumination, which are captured while the user is manually tilting the device around any ambient light source, such as the sun or a lamp. The lensless imaging scheme allows for sub-micron resolution imaging over an ultra-wide field-of-view (FOV). Image acquisition and reconstruction are performed on the device using a custom-built Android application, constructing a stand-alone imaging device for field applications. We discuss the construction of the device using a commercial smartphone and demonstrate the imaging capabilities of our system
SLIC Based Digital Image Enlargement
Low resolution image enhancement is a classical computer vision problem.
Selecting the best method to reconstruct an image to a higher resolution with
the limited data available in the low-resolution image is quite a challenge. A
major drawback from the existing enlargement techniques is the introduction of
color bleeding while interpolating pixels over the edges that separate distinct
colors in an image. The color bleeding causes to accentuate the edges with new
colors as a result of blending multiple colors over adjacent regions. This
paper proposes a novel approach to mitigate the color bleeding by segmenting
the homogeneous color regions of the image using Simple Linear Iterative
Clustering (SLIC) and applying a higher order interpolation technique
separately on the isolated segments. The interpolation at the boundaries of
each of the isolated segments is handled by using a morphological operation.
The approach is evaluated by comparing against several frequently used image
enlargement methods such as bilinear and bicubic interpolation by means of Peak
Signal-to-Noise-Ratio (PSNR) value. The results obtained exhibit that the
proposed method outperforms the baseline methods by means of PSNR and also
mitigates the color bleeding at the edges which improves the overall
appearance.Comment: 6 page
- …