A Novel Super Resolution Reconstruction of Low Reoslution Images Progressively Using DCT and Zonal Filter Based Denoising

In this technique a decision- tree- based impulse noise detector is used to detect the noisy pixels and an efficient conditional median filter is used to reconstruct

Although all the pixel based classification methods were applied on the original simulated image with scale factor 1 instead of scale fac- tor 6 which was utilized for both

First we are separating the image into R, G, B planes and then decomposing the image plane into 4 blocks and applying DCT transform over row mean vectors of each block

In experiments, we obtained results superior compared to competing approaches trained on generic image sets, which failed to reasonably scale satellite images with a high

We have compared six different reconstruction algorithms which are Bi-Cubic Interpolation method, Iterated Back Projection (IBP) algorithm, Points onto Convex Sets

The main assumptions made in the study were the selected training pixels to be pure elements of their representative land cover classes, the fine resolution pixels

Finally, the high resolution image and marker image as well as super- pixels are employed as input of Maximal Similarity based Region Merging (MSRM) to group the

Digital image processing is performed to recognize and count the cells flowing through the microfluidic channel. The processing includes three repeating steps to all the captured