"image processing model" is a correct and commonly used phrase in written English.
It refers to a model or system that is used to analyze, alter, or manipulate digital images. Example: "The researchers used a state-of-the-art image processing model to enhance the resolution and clarity of the satellite images they collected."
Combinations of four types of multispectral imagery ranging from 0.15 m resolution scanned color infrared aerial photography to 10 m resolution SPOT 5 multispectral imagery and three image processing models – per-pixel, object-based, and spectral mixture models – were tested.
For example, developers are more comfortable building image processing models in PyTorch while using Apache MXNet is used for recommendation systems dealing with clustering.
The efficiency of fixed-point-based proximity algorithms has been demonstrated in [5] and [20 22] for various image processing models.
An image-processing model appraised body shape.
An image-processing model was designed that calculates a parameter to assess body shape.
A clear picture of this process is needed to (i) inform functional architecture of image-processing models, (ii) identify the pathways available to support binocular rivalry, and (iii) generally advance our understanding of early vision.
New pixel- and region-based multiresolution image fusion algorithms are introduced in this paper using the Parameterized Logarithmic Image Processing (PLIP) model, a framework more suitable for processing images.
The GLS, such as the homomorphic multiplicative system (MHS) [10, 11], generalized mean filter [12], the log-ratio (LR) model [13], and the logarithmic image processing (LIP) model [14], has been studied since the late 1960s.
In the first article, the authors propose a generalization of the logarithmic image processing (LIP) model by introducing several parameters into the model.
