FSRCNN Model¶
FSRCNN
¶
Bases: Model
Fast Super-Resolution Convolutional Neural Network (FSRCNN).
An implementation of the model from the paper "Accelerating the Super-Resolution Convolutional Neural Network" by Dong et al.
Reference
https://arxiv.org/abs/1608.00367
__init__(channels: int = 1, upscale_factor: int = 3, non_linear_mapping_layers: int = 4, feature_dimensions: int = 56, feature_shrinking: int = 16)
¶
Initializes the FSRCNN model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
channels
|
int
|
The number of input and output image channels (e.g., 1 for grayscale, 3 for RGB). Defaults to 1. |
1
|
upscale_factor
|
int
|
The target upscaling factor. Defaults to 3. |
3
|
non_linear_mapping_layers
|
int
|
The number of mapping layers (m in the paper). Defaults to 4. |
4
|
feature_dimensions
|
int
|
The number of feature dimensions in the extraction layer (d in the paper). Defaults to 56. |
56
|
feature_shrinking
|
int
|
The number of channels in the shrinking layer (s in the paper). Defaults to 16. |
16
|
calculate_padding(kernel_size: int) -> int
¶
Calculates padding for a 'same' convolution.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
kernel_size
|
int
|
The size of the convolutional kernel. |
required |
Returns:
| Name | Type | Description |
|---|---|---|
int |
int
|
The padding required to keep the output dimensions the same as the input. |
Raises:
| Type | Description |
|---|---|
RuntimeError
|
If the kernel size is even. |